AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform.
5+ years of experience writing high-quality production code
Experience building high-performance distributed systems at a large scale (the more battle scars, the better)
Strong cloud skills
Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.)
Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!)
Ability to work in-person in our NYC or SF office.
Prior experience with Rust is nice to have, but not required.
Ability to participate in on-call rotation and respond to production incidents.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for Forward Deployed ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you'll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads — LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You're helping teams reach outcomes most engineers can't on their own.
The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the AI stack, and energy for working directly with customers on hard problems. You will:
Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal
Contribute to open-source projects — members of the team are active contributors to SGLang — and publish technical content that demonstrates Modal's capabilities across the AI stack
Collaborate with Modal's product and sales teams, contributing to the platform as both an engineer and a product stakeholder
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proof-of-concepts that make Modal's performance advantages tangible
2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go deep on at least one.
Strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership
Genuine interest in working directly with customers — you find it energizing to understand someone else's problem and help them solve it
Bonus: side projects, open-source contributions, or published work you're proud of in ML or systems performance
Willing to work in-person in New York City, San Francisco, or Stockholm
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We’re looking for business operations managers to join the team. This person will work closely with folks across marketing, sales, operations, and finance across a variety of initiatives to help scale the business in our next phase of growth. You'll be a generalist who gets in the weeds on all the business and operational aspects of a high-growth startup. In this role, you will:
Drive in-depth quantitative analyses to inform our pricing and packaging strategy.
Help spin up our deal desk and streamline enterprise deals.
Support the exec team on various finance functions, from investor relations to large cloud vendor negotiations to identifying cost optimization opportunities.
Implement new tools and processes to enable the GTM org to grow rapidly.
Get creative on a spectrum of ad-hoc projects like securing new office space in Manhattan.
We are looking for someone who:
Has 5+ years experience working in some kind of business function (e.g. banking, consulting, startup).
Has some exposure to tech and coding (in order to understand our product & customer, work with analytical tools, and build out custom tooling).
Is analytical, organized and detail-oriented, holds their output to a high bar of excellence.
Is eager to learn about a variety of different business functions and juggle many projects at once.
Is excited about working in-person in NYC.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you!
5+ years of experience writing high-quality, high-performance code.
Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).
Familiarity with Nvidia GPU architecture and CUDA.
Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc).
Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We’re looking for strong engineers with experience building developer tools that users love to work with. Our ideal candidate is someone with a demonstrated drive to build beautiful interfaces that enhance developer productivity.
5+ years of experience developing high-quality Python libraries with broad user-bases, ideally including some experience maintaining open-source software.
Knowledge of advanced Python features, especially async programming.
A strong product sense that manifests as a focus on developer ergonomics and productivity.
A high level of customer empathy, good communication skills, and an openness to working directly with our users to help solve their problems.
Ability to participate in on-call rotation and respond to production incidents.
Ability to work in-person in our NYC or Stockholm office.
Any of the following would be a plus:
Familiarity with modern data / ML / AI tools and workflows
Experience with Typescript, Go, or Rust
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for engineers with deep AI/ML and low-level systems experience who want to build the best technical support experience in the world. This isn't a traditional support role — it's an engineering role where you happen to be closest to our customers.
You'll split your time roughly 50/50 between working directly with customers and shipping fixes, features, and automation that improve Modal for everyone. When you help a customer debug a training run, you'll also fix the underlying issue in the platform. When you notice ten customers hitting the same friction point, you'll build the tooling or automation that eliminates it entirely.
This role is for people who solve problems, not people who answer tickets. The problems you encounter are deeply technical and arise from running some of the most demanding AI workloads in the world. You'll be a member of our engineering team, contributing production code alongside the engineers building the core platform. The difference is that your roadmap is shaped by what you learn at the frontier of customer experience. You will:
Ship code that matters. Fix bugs, build features, and create automation that improves the experience for every Modal user — not just the one who reported the issue.
Work directly with customers. Help developers and ML engineers debug, optimize, and architect their workloads across Slack, email, and calls.
Build scalable systems. Design tooling, dashboards, and automated workflows that make support efficient at scale — delighting customers at the most important moments.
Close the feedback loop. Translate patterns you see in the field into concrete improvements — docs fixes, API changes, or new feature proposals.
Contribute to open source and technical content. Write examples, build demos, and publish content that helps the broader community succeed on Modal.
Accomplished in key areas. You bring depth in either low-level infrastructure or ML/AI, and you're not lost in the other.
Low-level infrastructure experience. Operating systems, file systems, networking, performance profiling, cluster management and distributed systems.
AI/ML engineering experience. Training models, optimizing inference, working with GPUs, or building ML infrastructure.
Automation mindset. Your instinct when you see a manual process is to eliminate it and you have the engineering background to make that happen.
Clear communicator. Can explain a systems issue to a customer, write a crisp bug report, and draft documentation, all while collaborating internally to ship improvements.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for Forward Deployed Engineers on our engineering team who want to work at the intersection of deep infrastructure work and direct customer impact. As an FDE, you'll partner with leading AI companies and foundation labs on cloud architecture, networking, storage, containerization, sandboxing, and more — helping them design and ship production infrastructure on Modal's platform.
The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the infrastructure stack, and energy for working directly with customers on hard problems. You will:
Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and deploy massive-scale production workloads on Modal
Lead technical discovery and architecture sessions with prospective and existing customers
Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform
Collaborate with Modal's product and sales teams, contributing to the platform as both an engineer and a product stakeholder
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proof-of-concepts that make Modal's infrastructure advantages tangible
3+ years of professional software engineering experience
Hands-on experience with cloud platforms (AWS, GCP, Azure) — compute, storage, networking, and container orchestration (Docker, Kubernetes)
Familiarity with distributed systems architecture, data pipelines, and Infrastructure as Code (Terraform, Pulumi, CloudFormation)
Strong communicator who can go deep on systems architecture with an infrastructure team and clearly articulate tradeoffs to technical leadership
Genuine interest in working directly with customers — you find it energizing to understand someone else's problem and help them solve it
Bonus: experience leading large-scale migration efforts, open-source contributions, or side projects you're proud of
Willing to work in-person in New York City, San Francisco, or Stockholm
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Modal builds AI infrastructure products that developers love. That's how we grew so quickly, and why word of mouth remains one of our most important channels today.
In this role, you will primarily create and distribute technical content that is unique, educational, and practical. This content will be the first Modal touchpoint for many of our users. We want to not only showcase the power and developer experience of Modal, but also serve as a trusted resource for them when implementing new AI technologies.
In this role, you will:
Distill the latest advancements in AI technology and educate developers on how to incorporate them.
Give demos/talks about Modal and adjacent tools at developer events.
Engage with users in our community, both online (X, LinkedInReddit, Slack) and at in-person events.
Build relationships, integrations, and joint marketing activities with other developer-focused companies
Set objectives that are aligned with the greater GTM team and track the impact of the initiatives you work on.
We are looking for someone who:
3+ years as a software engineer
Is energized by the AI developer community and wants to help developers adopt new technologies.
Loves teaching.
Has excellent technical communication skills.
Is metrics-driven and takes quantitative approaches to prioritizing initiatives.
Is excited about working in-person in the NYC, SF or Stockholm office.
Bonus: you're not afraid to think outside the box when it comes to compelling technical content.
Bonus: you already have a developer following on social media!
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We’re hiring an Enterprise Account Executive to accelerate Modal’s growth with the world’s most innovative AI companies. This is a high-impact role where you’ll own the full sales cycle—from building pipeline to closing large, strategic enterprise deals. You’ll partner directly with our founders, engineering, and product teams to help customers harness Modal’s infrastructure to train, deploy, and scale AI applications.
You’ll be expected to operate as a builder: developing new relationships, shaping our GTM motion, and serving as the voice of the customer inside Modal. The ideal candidate is both technically curious and commercially driven—equally comfortable in a room with C-level executives and with machine learning engineers. In this role, you will:
Drive new business by generating pipeline, negotiating, and closing complex enterprise deals
Build deep, trusted relationships with technical and executive stakeholders at leading AI companies
Run proof-of-concepts and pilots in close collaboration with our solutions and engineering teams
Expand existing accounts by identifying growth opportunities and helping customers scale their workloads
Bring structured customer feedback to influence Modal’s product roadmap and GTM strategy
Contribute to the foundation of Modal’s sales process, culture, and playbooks as we scale the team
7–10+ years of enterprise software sales experience, ideally in infrastructure, ML/AI, or developer-focused platforms
Track record of consistently exceeding $1M+ annual quotas and closing six- and seven-figure enterprise deals
Strong technical acumen—able to communicate infrastructure and AI concepts with both engineers and executives
Experience leading proof-of-concepts and managing complex procurement processes
Excellent communication, negotiation, and relationship-building skills
Comfortable in an early-stage, fast-moving environment; excited to help shape systems and processes from the ground up
Ability to work in-person from our office 5 days a week
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Modal builds the infrastructure that lets engineers run AI workloads without the usual pain. To do this well, we need exceptional people – and that’s where you come in.
As the first dedicated GTM recruiter on our Talent team, you’ll own sales, GTM, and other G&A searches end-to-end. You’ll work closely with our Head of Talent, founders, and GTM leads to shape how we hire and help bring in the people who will define what Modal becomes.
What you’ll do:
Drive full-cycle recruiting for key hires across GTM and G&A functions (sourcing, pitching, guiding interviews, and closing candidates)
Partner with GTM leaders to understand the real work and calibrate on what great looks like
Help set our hiring bar and how we evaluate talent
Execute creative top-of-funnel strategies that resonate with a strong community of experienced GTM talent
Deliver a fast, respectful, honest candidate experience
Bring an equity lens to every stage of the process
Use data and market context to keep searches on track and expectations aligned
Support candidates as a trusted guide through high-stakes decisions
You know GTM recruiting inside and out and enjoy challenging searches
You’re curious – you ask the right questions and love understanding how things actually work
You influence with insight and honesty, not by simply having the loudest voice in the room
You move quickly and communicate clearly, even when things get messy
You’re a strong closer who builds trust early and stays ahead of concerns
You’re steady when priorities shift and can recalibrate without losing momentum
You care about people and want them to have a thoughtful, fair, and human experience throughout the process
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Modal is building the future of serverless computing, and the brand that carries that story is still taking shape — You'll join Modal's newly formed Brand team inside our design org as one of its first senior hires, working directly with the Director of Brand Design and Head of Design to build a brand developers recognize instantly and remember.
As a staff-level Brand Designer, you will have major influence over every brand surface: the marketing website, campaigns, events, editorial projects like the GPU Glossary and forthcoming publications, and out-of-home work as we scale into larger formats. You'll also be a beacon to external agencies, representing Modal's internal creative voice and making sure the work translates into a system we can actually build on. And as the studio grows, you'll help set its craft standard — guiding and mentoring earlier-career designers and shaping how we critique, ship, and show work.
Define and evolve Modal's visual identity across web, marketing, campaigns, and editorial — building a cohesive, distinctive system rather than one-off assets
Lead concept and execution for brand campaigns across digital and out-of-home, from paid social to large-format outdoor, ensuring the work is consistent, distinctive, and built to perform
Own branded experiences for events and GTM: environments, collateral, swag, and conference presence that feel unmistakably Modal
Help codify the brand as it matures: design language, principles, and the rules that hold it together
Mentor and critique the work of other designers, helping the studio's output stay coherent and ambitious as it scales
Move quickly without sacrificing quality
7+ years of brand design experience, building brands for technically complex products — developer tools, infrastructure, or AI. You know how to make something deeply technical feel compelling and clear.
High influence, Low ego: You work well with others, share credit freely, and act as force for positivity within the brand team
A portfolio that demonstrates exceptional typography, color, composition, and a recognizable point of view
Experience designing brand systems that extend from identity to motion to web – the more the better
Ability to interpret and contribute to brand strategy
Someone who takes ownership seriously: you drive work to completion, raise the quality bar, and build structures that let others do the same
Strong taste, and the ability to defend it. Has opinions, backs them up, takes feedback well
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for strong backend engineers who love building a developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day.
Experience building and shipping modern web applications end-to-end. We care more about what you’ve built than how many years you’ve been building.
Comfort working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics.
Deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes, training and inference for large language (LLM) and diffusion models.
Experience with at least one of: billing/payments systems, B2B SaaS tooling, or enterprise software, or LLM / diffusion models inference and training loads.
Strong product instincts; you think about customer problems, not just tickets.
Ability to participate in on-call rotation and respond to production incidents.
Ability to make good tradeoffs between shipping fast and building for scale.
Ability to work in-person in our NYC or SF office.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform.
5+ years of experience writing high-quality production code
Experience building high-performance distributed systems at a large scale (the more battle scars, the better)
Strong cloud skills
Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.)
Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!)
Ability to work in-person in our Stockholm office.
Prior experience with Rust is nice to have, but not required.
Ability to participate in on-call rotation and respond to production incidents.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Modal is seeking an experienced Forward Deployed Engineer (FDE) to partner with our sales team and drive technical sales success. As an FDE, you will be the technical voice in our sales process, working directly with Account Executives to help enterprise customers understand how Modal can transform their AI/ML infrastructure. You will:
Partner with Account Executives to identify, qualify, and close strategic enterprise opportunities
Lead technical discovery sessions with prospective customers to understand their current infrastructure, pain points, and requirements
Design and present compelling technical solutions that demonstrate how Modal addresses customer needs
Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform
Conduct technical demos, experiments, and proof-of-concepts that showcase Modal's capabilities
Navigate complex technical evaluations and address security, compliance, and integration concerns
Build trusted advisor relationships with technical decision-makers including CTOs, VPs of Engineering, and ML Engineering leads
Collaborate with product and engineering teams to communicate customer feedback and influence product roadmap
Support contract negotiations by providing technical expertise on implementation timelines, resource requirements, and success metrics
5+ years of experience in solutions engineering, sales engineering, or customer-facing technical roles
Deep hands-on experience with cloud platforms (AWS, GCP, Azure) including compute, storage, networking, and managed services
Strong knowledge of containerization technologies (Docker, Kubernetes, container orchestration)
Experience with databases (SQL/NoSQL), data pipelines, and distributed systems architecture
Understanding of ML/AI infrastructure challenges including model training, inference, and MLOps workflows
Familiarity with Infrastructure as Code (Terraform, Pulumi, CloudFormation) and CI/CD pipelines
Proven track record of supporting enterprise software sales cycles ($100K+ ACV)
Exceptional presentation and communication skills with ability to explain complex technical concepts to both technical and business audiences
Strong business acumen with understanding of enterprise buying processes and procurement
Experience building migration strategies and implementation roadmaps for large-scale infrastructure changes
Ability to work effectively with cross-functional teams including sales, product, and engineering
Experience selling or implementing serverless computing, container platforms, or ML infrastructure solutions preferred
Willingness to travel up to 30% for customer meetings and industry events
Ability to work in-person in our Stockholm office
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
At Modal, we sell cloud services atop which our customers run their critical production systems. As a rapidly growing new cloud infrastructure company, we seek to improve our reliability dramatically while scaling the size of our platform, customer base, and our team.
This role is for people who are deep systems thinkers, love stacking nines, and thrive from making others move faster at scale. Responsibilities include:
Identifying architectural changes to improve reliability and performance.
Fostering a culture of reliability across Modal’s engineering organization.
Defining and implementing operational processes such as deployments, upgrades, etc.
Operating systems like Kubernetes, Postgres, Redis, etc.
Participating in on-call rotations, and responding to production incidents.
5+ years of experience writing high-quality production code.
2+ years of on-call experience for critical production services.
Strong cloud skills, and deep familiarity with at least one hyperscaler cloud (AWS preferred).
Familiarity with auto scaling, fleet management, and capacity planning at scale.
Experience operating databases, monitoring, CI/CD, and other infrastructure, at scale
Experience owning and scaling Kubernetes clusters to thousands of nodes a plus.
Experience with systems safety research (e.g. STAMP) and control theory a plus.
Ability to work in-person in our NYC or Stockholm offices.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for an Engineering Manager to lead a team of highly experienced engineers building the infrastructure that powers Modal's serverless GPU platform. This is a hands-on leadership role — expect to split your time between technical contribution and people management depending on what the team needs. You'll set direction, remove blockers, and build a strong engineering culture as your team tackles hard problems in distributed computing, large-scale data handling, and performance optimization.
You're an experienced engineering leader who stays close to the work and builds alongside your team when it counts. You earn trust through technical depth, not title. You communicate clearly, help strong engineers move fast without cutting corners, and stay calm and pragmatic under pressure. You care as much about how your team gets to an answer as the answer itself.
Team
Recruit, hire, and grow a high-performing team of cloud platform engineers; run regular 1:1s focused on coaching, feedback, and career growth.
Set clear performance expectations, hold a high bar, and build an environment where engineers do their best work.
Foster a culture of ownership, accountability, and continuous improvement.
Technical Direction
Drive day-to-day technical decisions through design reviews, code reviews, and architectural discussions.
Translate the infrastructure roadmap into clear team priorities and milestones, and hold execution against them.
Establish standards for reliability, performance, and operational excellence; ensure the team owns projects end-to-end, from spec through production.
Push for good judgment on tooling and architecture, with a bias against unnecessary complexity.
Cross-Functional & Incident Leadership
Partner with product and engineering to align infrastructure work with business priorities; represent your team's progress, capacity, and tradeoffs clearly to leadership.
Serve as the escalation point for major incidents; drive resolution with urgency and ensure the team learns systematically, feeding those learnings back into infrastructure improvements.
10+ years of industry experience, including 3+ years in a leadership role
Track record building high-performance distributed systems at scale
Strong background in cloud infrastructure
Deep knowledge of low-level OS foundations (Linux kernel, file systems, containers, etc.)
Proficiency in a systems-level language (Rust, C, C++, or Java)
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Modal is hiring a high-impact Solutions Architect to drive technical strategy across our most strategic enterprise accounts.
You will operate as the executive technical counterpart to Enterprise Account Executives, leading complex evaluations, shaping infrastructure modernization roadmaps, and driving multi-product adoption across AI/ML workloads.
This role is not demo support. It is a strategic, consultative position requiring strong architectural depth, executive presence, and the ability to influence 7–8 figure infrastructure decisions. You will work directly with CTOs, VPs of Engineering, and ML platform leaders to help them rethink how AI infrastructure should be built and operated.
If you thrive in high-velocity technical sales environments and want to shape the infrastructure layer powering modern AI companies, this role is for you.
Own the technical strategy for large, complex enterprise accounts
Partner with Enterprise Account Executives to drive 6–7+ figure opportunities from qualification through close
Lead deep technical discovery across platform, ML, DevOps, and infrastructure stakeholders
Architect end-to-end migration strategies from AWS, GCP, or Azure to Modal’s serverless infrastructure
Drive executive-level technical conversations that connect infrastructure architecture to business impact (velocity, cost efficiency, reliability)
Design and oversee proof-of-concepts that demonstrate production-grade scalability and performance
Navigate security reviews, compliance discussions, and procurement processes with confidence
Build durable relationships with technical champions and executive sponsors
Orchestrate cross-functional virtual account teams (Sales, Product, Engineering, Support) to ensure successful adoption
Influence Modal’s product roadmap by surfacing structured enterprise feedback
Mentor other Solutions Architects and contribute to technical best practices across the team
5+ years in Solutions Engineering, Sales Engineering, or enterprise-facing technical roles in infrastructure, cloud, or developer platforms
Demonstrated success driving complex enterprise sales cycles ($250K+ ACV; experience with $1M+ ARR accounts strongly preferred)
Experience working with large, sophisticated Bay Area technology companies or global enterprises
Deep expertise in cloud infrastructure (AWS, GCP, Azure) including compute, networking, storage, and managed services
Strong architectural understanding of containers and orchestration (Docker, Kubernetes)
Experience designing distributed systems and production data platforms
Strong working knowledge of ML/AI infrastructure (training pipelines, inference systems, GPU workloads, MLOps)
Experience leading cloud migration or infrastructure modernization initiatives
Familiarity with Infrastructure as Code (Terraform, Pulumi, CloudFormation) and CI/CD systems
Production programming experience in Python strongly preferred
Executive-level communication skills with the ability to influence technical and business stakeholders
Proven ability to lead cross-functional virtual teams in high-stakes account environments
Strong business acumen and understanding of enterprise procurement and buying processes
Willingness to travel up to 30%
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for Forward Deployed ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you'll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads — LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You're helping teams reach outcomes most engineers can't on their own.
The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the AI stack, and energy for working directly with customers on hard problems. You will:
Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads on Modal
Contribute to open-source projects — members of the team are active contributors to SGLang — and publish technical content that demonstrates Modal's capabilities across the AI stack
Collaborate with Modal's product and sales teams, contributing to the platform as both an engineer and a product stakeholder
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proof-of-concepts that make Modal's performance advantages tangible
2+ years of professional ML engineering experience, ideally with hands-on work in inference optimization, model training, GPU programming, or ML infrastructure
Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go deep on at least one.
Strong communicator who can go deep on technical architecture with an engineering team and clearly articulate tradeoffs to technical leadership
Genuine interest in working directly with customers — you find it energizing to understand someone else's problem and help them solve it
Bonus: side projects, open-source contributions, or published work you're proud of in ML or systems performance
Willing to work in-person in Stockholm
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're hiring a Compute Strategy and Operations lead to own how Modal plans for and acquires GPU and CPU capacity. You'll size our infrastructure needs ahead of demand, source supply across hyperscalers, neoclouds, and datacenter operators, and negotiate and close the contracts to secure it. The compute you secure directly determines what Modal can sell and build.
In this role, you will:
Own end-to-end procurement of GPU and CPU capacity across hyperscalers, neoclouds, and datacenter operators
Build and maintain a strong pipeline of supplier relationships
Evaluate supply options on price, availability, hardware specs, networking capabilities, and SLA terms
Negotiate and close contracts: reserved capacity agreements, spot arrangements, MSAs, DPAs, and order forms
Work closely with our engineering teams to translate technical requirements into procurement specs
Track market pricing, availability trends, and supplier dynamics, and serve as a trusted advisor to leadership on infrastructure procurement decisions
Fluency in the AI infrastructure landscape: hyperscalers, neoclouds, and the broader GPU supply market
Technical understanding of what you're buying: GPU architectures (NVIDIA H100/H200/B200, etc.), networking (InfiniBand vs. RoCE, fabric topology), and SLA structures
Strong commercial instincts. You know how to find leverage, structure a deal, and close it
Ability to work cross-functionally with finance and engineering teams on commercial terms and technical requirements
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We’re looking for an Infrastructure Security Engineer to design and secure the core systems that power our platform. This role focuses on building security directly into our infrastructure—from container isolation and orchestration to identity and secrets management in a multi-tenant, cloud-native environment.
You’ll work closely with engineering teams to define secure primitives and ensure our platform is resilient, scalable, and trustworthy by design.
This is a hands-on, deeply technical role focused on real systems, not compliance or policy.
Platform & Runtime Security
Design and improve isolation mechanisms for multi-tenant workloads (containers, sandboxing, execution environments)
Strengthen boundaries between customers, workloads, and internal systems
Identify and mitigate risks in distributed, dynamic compute environments
Container & Orchestration Security
Secure and harden containerized workloads and orchestration systems (e.g., Kubernetes or similar)
Improve workload isolation, scheduling boundaries, and runtime protections
Evaluate tradeoffs in multi-tenant execution models
Identity & Access Management
Design and improve authentication and authorization systems across services
Implement strong service-to-service identity and least-privilege access patterns
Improve access controls across infrastructure and internal systems
Secrets & Key Management
Build and maintain systems for securely managing secrets, tokens, and credentials
Improve rotation, auditing, and access controls
Reduce secret sprawl and integrate secure patterns into developer workflows
Cloud & Infrastructure Security
Secure cloud environments across providers (AWS, GCP, etc.) with a focus on consistency and portability
Improve network boundaries, service segmentation, and access controls
Embed security into infrastructure-as-code and deployment systems
Engineering Partnership
Work closely with product and infrastructure teams to design secure systems from the ground up
Review architecture and code for security risks and provide actionable guidance
Identify patterns in risks and drive cross-cutting improvements
Core Experience
Experience securing cloud-native infrastructure and distributed systems in production
Background in infrastructure, backend, or security engineering
Experience working in multi-tenant or high-scale environments
Technical Depth
Strong understanding of containerization and orchestration systems (e.g., Kubernetes or similar)
Experience designing or securing isolation mechanisms in multi-tenant systems
Solid understanding of authentication, authorization, and service identity models
Experience with secrets management and secure handling of credentials
Strong foundation in networking concepts (segmentation, service communication, access boundaries)
Mindset
Builder mentality, you design and implement, not just review
Pragmatic approach to security in fast-moving environments
Comfortable working deeply with engineers and influencing system design
Experience with sandboxing or runtime isolation technologies (e.g., gVisor, Firecracker, seccomp, or similar)
Familiarity with kernel-level or low-level isolation primitives
Experience securing Kubernetes or similar orchestration systems in production
Background in developer infrastructure, compute platforms, or multi-tenant systems
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for a Growth Engineer to own the technical foundation of Modal's marketing and developer-facing web surfaces: the marketing site, docs site, growth landing pages, high-profile microsites, forms, analytics instrumentation, and the integrations that help users discover, understand, and get started with Modal.
This is a frontend-heavy role for someone with strong product taste, web engineering craft, and a business-owner mindset. You'll partner with Product Engineering, Design, Data, and Growth to ship polished, measurable web experiences from high-profile projects like the GPU Glossary and LLM Engine Advisor to internal tooling that helps teams publish content faster.
When this role is going well, Modal launches new pages, docs experiences, campaigns, and experiments quickly without sacrificing performance, craft, or measurement.
Own and evolve Modal's marketing site, docs site, and technical web experiences as high-quality engineering systems, not static content.
Build bespoke interactive projects with engineers, turning deep technical work into useful web experiences for developers and agents.
Own Instrument all public-facing content, all the way from human-facing marketing pages to agent-targeted, self-improving documentation.
Improve internal publishing flows so more teams can ship quality content quickly while keeping the site maintainable.
Evidence of building and shipping modern web applications end-to-end. We care more about what you've built than years of experience.
Experience with agentic development flows where agents consume marketing content and docs to help developers build on Modal.
You can build polished, responsive web experiences without needing every detail specified.
Business-owner mindset with strong data intuition: you think about conversion, activation, developer trust, speed of launch, and quality of measurement and tradeoff inherent to each.
Excited about developer-facing AI infrastructure and building web experiences that feel credible to engineers.
Ability to work in-person in our NYC office.
Deep knowledge of SEO, AEO, structured content, or agentic discoverability.
Experience with marketing sites, docs sites, or product-led-growth surfaces at a dev tools / infra / AI company.
Familiarity with our stack: Svelte/SvelteKit, TypeScript, Tailwind, Vite, MDX/SVX content systems, headless CMS (Sanity), Playwright/Vitest.
Familiarity with our integrations: Segment, Snowflake, PostHog, Sentry, Algolia/DocSearch, Fillout, Pylon, Ashby.
Experience designing growth experiments, A/B tests, or funnel instrumentation.
A portfolio of polished sites, docs systems, interactive demos, or growth experiments.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We’re looking for an Engineering Manager to lead a group of highly experienced engineers. This is a hands-on leadership role where you’ll spend roughly half your time on technical contribution and half on people management, depending on the need. You’ll work closely with the team to set direction, remove blockers, and foster a strong engineering culture as they tackle complex systems challenges in distributed computing, large-scale data handling, and performance optimization.
We think you are an experienced engineering leader who thrives close to the work and enjoys building alongside their team when needed. You earn trust through technical depth, communicate with clarity, and help great engineers move fast and make sound decisions. You thrive in a fast paced environment, you are pragmatic, calm under pressure, and focused on impact.
At least 10 years of industry experience, including 3 years experience in a leadership role
Experience building high-performance distributed systems at a large scale
Strong background in cloud infrastructure
Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.)
Proficient in systems-level languages such as Rust, C, C++, or Java
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Most of the value of owning a model shows up at serving time. We're building a platform that covers the whole life of an LLM -- train it, deploy it, observe it -- and inference is where teams feel the difference every day. We already run elastic inference, sandboxes, distributed volumes, and multi-node training, and we control the infrastructure underneath, so the serving stack is ours to shape rather than something we resell.
You will do hands-on inference research at Modal, working with the research lead to pick high-impact bets and owning them end to end. The bets that matter most are the ones that move cost per token and tail latency on the workloads our customers actually run.
Own end-to-end inference research bets: speculative decoding, disaggregated prefill/decode, quantization (FP8, INT4), KV-cache and memory management, autoscaling for spiky serverless traffic, and whatever else the research agenda calls for.
Train custom speculators against real production traffic and feed what you learn back into target models -- acceptance length is the metric that decides the win.
Work directly with customers alongside our Forward Deployed Engineers to deploy and tune models, and bring what you learn back into the research.
Carry and expand collaborations with outside research labs, for example:
our work with ZLab on DFlash, a speculator design built on KV injection and blockwise parallel drafting
our work with SGLang on specdec and multimodal inference performance
our work on Flash Attention 4 kernels
Work with engineering to turn frontier serving techniques into products: primitives for disaggregation, fast weight refresh for models that keep training after deployment, observability for quality and latency in production, or even a next-generation inference engine.
Help shape the research agenda. None of the above is prescriptive; your work will help guide our future.
A research-leaning or systems background in LLM inference, with work you can point to.
Fluency in the LLM serving stack, from kernels and quantization up to schedulers and autoscaling.
A record of shipping research or systems that other people build on, whether in a lab or in industry.
The drive to independently take a research bet from idea to result, working in the open with the rest of the team.
Ability to work in-person, in our NYC or San Francisco office.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work.
We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.
A PhD in computer science, machine learning, or a related field. Candidates with a master’s degree and significant research or industry experience will also be considered.
A demonstrated record of research accomplishments in reinforcement learning, machine learning, foundation models, or related fields.
Experience with large-scale training and inference infrastructure, including distributed systems and multi-node GPU clusters.
Experience developing, training, optimizing, or deploying state-of-the-art large-scale models.
First-author publications at leading venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR.
A mission-driven mindset and a strong desire to translate research advances into meaningful product impact.
A collaborative spirit and the ability to work effectively across research and engineering teams.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g., Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
About the Role
We're seeking a Revenue Operations Manager with a strong track record, a builder's mindset, and a bias for action to join our in-person team in New York or SF.
This is a high-impact, hands-on role. You'll own the entire revenue operations function, from top-of-funnel lead routing through deal close and commission administration. You'll work closely with our Head of Finance & People Ops and sales leadership to build the systems, dashboards, and processes that scale our go-to-market motion.
What You'll Do:
Own the lead routing process from inbound and partnering with marketing to ensure proper attribution
Run effective territory management & strategy for Geo based decisioning
Support & strategise every aspect of revenue operations in your territory
Own the strategy for capacity forecasting, inputs, throughputs & outputs being the conduit back to finance in the US for effect capacity forecasting
Input and strategy for comp based decisioning and benchmarking
Support leadership in overall regional operations & finance strategy across all functions, sales, pre, ecosystem etc
Evaluate new tools in market and be responsible for improving the efficiency and effectiveness of the sales team
Build and maintain dashboards, leaderboards, and pipeline reporting to give leadership real-time visibility.
Monitor deal progression and hold reps accountable for CRM hygiene and accuracy.
Support deal reviews at the pricing and negotiation stages, coordinating with outside counsel as needed.
Own the closed-won process from signature through handoff to post-sale.
Administer commission plans across the sales org, ensuring accurate and timely calculations and serving as the source of truth for comp-related questions.
What You Bring:
A builder's mentality; you identify gaps, propose solutions, and execute without being asked twice.
The ability to zoom out and think strategically while staying close enough to the work to get your hands on the keyboard.
An AI-forward mindset: you actively use AI in your work and are excited to pioneer its use in go-to-market operations.
Strong communication and collaboration skills; you earn trust across Sales, Marketing, and Finance.
A player-coach orientation; you're not above the details and you're excited to own the work yourself
Requirements:
3–7 years in Sales Operations or Revenue Operations at a high-growth usage based sales organization
You've built core GTM infrastructure before: lead routing, CRM architecture, pipeline reporting, commission administration, deal desk.
Deep proficiency in workflows, lifecycle stages, custom properties, data hygiene.
You've owned commissions end-to-end: building the model, calculating payouts, fielding rep disputes, and keeping it reconciled with Finance.
You've run a deals desk, you know what makes a contract clean and you catch errors before they become problems.
Strong in Excel or Google Sheets; you can build a pipeline coverage model and know which metrics actually matter.
You've earned trust with a sales team by making their lives easier while holding them accountable to process.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for a People Operations Generalist to join our growing People team. You'll touch the employee lifecycle end-to-end — from offer acceptance through offboarding — while helping to build the processes and documentation that let our People function scale with the business.
This is a great fit for a highly organized, systems-oriented people person who thrives in a fast-paced environment and wants to build operational foundations, not just maintain them.
What you’ll do
Own and continuously improve the new hire onboarding experience, ensuring employees are set up for success and internal tasks are tracked and completed on time.
Serve as a first point of contact for employee questions across the full HR spectrum, triaging and routing more complex issues to the right People team member or external partner.
Maintain and improve self-service resources (FAQs, Notion pages, etc.) so employees can find what they need quickly — and always feel supported even when no one's in the room.
Support rollouts of new People programs and policies, including communication planning, manager enablement, and documentation.
Help bring employee experience programs and events to life, from team offsites to milestone recognition, that reinforce culture and make people feel valued.
Process employee changes (promotions, equity refreshes) accurately and on time, including preparing updated employment letters and maintaining compliant employee files.
2+ years of People Operations experience, preferably at a high-growth startup.
Demonstrated ability to build and document HR processes from the ground up, not just execute existing ones.
Strong written communication: you write clearly, keep things simple, and know your audience.
Experience managing employee data with accuracy and discretion.
Comfortable handling competing priorities without losing track of the details.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g., Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
About the Role:
As a Manager, Enterprise Sales, you will lead and scale our enterprise sales team, driving strategic revenue growth with a consultative, customer-first approach. You will oversee complex deal cycles, coach Enterprise Account Executives, and build the motion that wins high-impact, multi-stakeholder deals in a rapidly evolving AI landscape.
What You’ll Do
Lead, mentor, and develop a team of Enterprise Account Executives, fostering a culture of performance, strategic thinking, and collaboration
Own and guide the full enterprise sales cycle, from targeted outbound and discovery to multi-threaded navigation, negotiation, and close
Build and refine enterprise sales playbooks, qualification frameworks, and forecasting models that increase accuracy and velocity
Collaborate cross-functionally with Product, Marketing, and Engineering to align on go-to-market strategy, unblock enterprise requirements, and deliver seamless customer experience
Drive pipeline generation strategy and help shape top-of-funnel programs
Establish enterprise sales processes, performance metrics, and best practices that ensure consistent quota attainment across the team
Who You Are
A proven enterprise sales leader with experience closing large, complex deals and managing high-performing teams
Skilled in multi-threading, stakeholder alignment, and navigating procurement, legal, security review, and executive-level conversations
A strong coach who helps Account Executives sharpen discovery, messaging, deal strategy, and negotiation
Highly strategic and structured, able to build repeatable systems and scalable enterprise sales motions
Comfortable owning forecasts, running deal reviews, and driving predictable revenue outcomes
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.
Preferred Qualifications:
Currently pursuing a PhD in computer science, machine learning, or a related field.
A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas.
Experience developing and evaluating large-scale models or machine learning systems.
Familiarity with distributed training, large-scale inference, or multi-GPU environments.
Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR.
Strong programming and engineering skills, with the ability to translate research ideas into working implementations.
A collaborative, mission-driven mindset and the ability to work effectively across research and engineering teams.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for a Detection & Response Engineer to build the systems that help us identify, investigate, and respond to threats across our platform.
This is an engineering role focused on automation. You'll build detections, investigation tooling, and response capabilities that scale with our infrastructure, using AI where it meaningfully improves signal, investigation speed, and operational effectiveness.
You'll work closely with infrastructure, platform, and security engineers to ensure every incident makes the platform more resilient.
Design and build high-fidelity detections for attacks, abuse, and anomalous behavior across our infrastructure and production systems
Continuously improve detections based on telemetry, threat intelligence, and lessons learned from incidents
Improve visibility across cloud infrastructure, containers, identity systems, and production services
Lead or participate in investigations spanning production infrastructure, cloud environments, and internal systems
Build playbooks and automation that reduce investigation time and improve response consistency
Drive post-incident improvements that eliminate entire classes of future incidents
Build internal tooling that improves detection, investigation, and response workflows
Leverage LLMs to automate repetitive analysis, accelerate investigations, and surface actionable insights from security telemetry
Improve the collection, quality, and usability of security telemetry across the platform
Partner with engineering teams to ensure new systems are observable and secure by default
Help teams instrument services with the telemetry needed for effective detection and response
Drive security improvements that make the platform easier to defend over time
Experience in detection engineering, incident response, security engineering, or software engineering with a strong security focus
Strong software engineering skills with experience building production systems
Experience investigating security incidents in cloud-native or distributed environments
Familiarity with modern cloud infrastructure, Kubernetes, Linux, and networking
Experience building detections using logs, telemetry, behavioral signals, or large-scale event data
Strong SQL skills for investigating security events and developing detections
Interest in applying AI and LLMs to detection, investigation, and response, including understanding emerging threats involving AI-powered systems
Strong written and verbal communication skills
Experience building AI- or LLM-powered security tooling
Experience with SIEM, SOAR, or EDR platforms
Experience with Kubernetes security or large-scale cloud infrastructure
Experience with threat hunting, malware analysis, or digital forensics
Experience contributing to security operations in a high-growth engineering organization
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
About Talent at Modal
Modal is growing fast, and the programs that bring people in and set them up to succeed are still being built. You'll join the Talent team as one of its first hires focused purely on programs; working closely with recruiting and leadership to build the events, internship, and campus presence that shape how the best people discover and experience Modal for the first time.
The Role
As Talent Programs Manager, you will own Modal's talent events, our intern program, and our presence at career fairs, end-to-end. This is a build-from-the-ground-up role for someone who wants full ownership rather than an existing playbook to execute. You'll work directly with recruiters, hiring managers, and marketing to make sure every program ladders up to real hiring outcomes, and you'll be the person who makes candidates' and interns' first experience of Modal a great one.
What You'll Do:
Own Modal's talent events strategy; meetups, recruiting mixers, tech talks, and community events, from concept through post-event follow-up
Design and run the intern program end to end: recruiting timeline, project scoping with hiring managers, mentorship matching, and conversion
Represent Modal at career fairs and campus events, and recruit and prep employee brand ambassadors to join you
Build and maintain relationships with university programs, bootcamps, and community organizations that feed our pipeline
Manage program budgets and vendor relationships; venues, swag, catering, event platforms
Track and report on program metrics: event pipeline conversion, intern conversion rate, cost per hire from programs
Move quickly without sacrificing the candidate or intern experience
Requirements:
3+ years running recruiting/talent programs, events, or university relations, with a track record of owning a program end-to-end including budget and vendors
High influence, low ego: you build relationships easily across recruiting, hiring managers, and marketing, and share credit freely
Strong project management skills; comfortable juggling multiple concurrent events and timelines without dropping details
Excellent written and verbal communication; comfortable representing Modal externally
Someone who takes ownership seriously: you drive programs to completion and build structures that let them scale
Based in or willing to relocate to NYC, in-office
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since last September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are hiring our first People team member on the ground in London - the person who builds and runs the people foundation for the UK as our London office grows from a small team today into one of Modal’s core hubs.
You‘ll help build out of the people function in London end to end, ensuring we create the best environment for our team. You will be the person who meets a new hire on day one, the person anyone on the team goes to when something comes up, and the owner of all things people operations (systems, benefits, onboarding).
This is a foundational, high-ownership role. We’re looking for someone who wants to build a region, and who is energized by being the go-to person from everything People in London.
Own core people operations for the London team - onboarding, offboarding, payroll coordination, and benefits administration so every experience is smooth and nothing falls through the cracks.
Be the first, trusted point of contact for London employees on questions about policies, benefits, pay, and people matters, handling sensitive topics with clarity, discretion, and pragmatism.
Support managers on the ground: help them set their teams up well, spot early performance or people issues, and know when to loop in the broader People team.
Keep our people data clean and current in our systems (departments, titles, managers) as we stand up a single global source of truth, and help operationalize the new HRIS locally.
Make sure our UK benefits are competitive and in line with the local market, and that time-off and policy messaging is clear and consistent for the London team.
Own the people side of building out Modal’s UK presence - partnering with external vendors and global people operations to keep us compliant with UK employment.
Keep the London office running smoothly day to day (space, supplies, setup) and be a thoughtful steward of a welcoming, functional environment.
Own local events, team bonding, and the in-office experience that makes London somewhere people want to be.
Coordinate with vendors and building management as needed.
Own local recruiting operations for the London office in partnership with our global Talent team, including interview logistics and candidate experience on the ground.
5+ years experience in a People Ops or People & Talent generalist role, ideally in a fast-growing tech environment
Working knowledge of UK employment norms, compliance, and labor environments
Comfortable working autonomously and owning outcomes with minimal oversight - you see gaps and fix them
Strong organizational skills and attention to detail - you notice what others miss
Clear, thoughtful communicator with candidates, employees, and stakeholders
Comfortable shifting between strategic thinking and hands-on execution
Excited to be a foundational hire and help build people processes from the ground up
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We’re growing our Data team and are looking for our first few key hires to build self-serve data tools and drive business strategy in the right direction.
The mission of the Modal Data team is to make it easy to track company goals, make evidence-backed decisions, and prioritize the right work. We do this via:
Self-serve AI analytics tools (Hex, Snowflake)
Embedding with teams as a “data adviser”, providing strategic analysis and consulting
Contribute to building the most modern analytics stack in Data today to support AI-driven self-serve analysis, key metrics tracking, and external customer reporting
Influence work on new products like LLM Inference Endpoints through product analytics tracking
Identify millions of dollars of cost savings and optimization across our tools and financial operations
Write data pipelines that power the operations of our business, such as our cloud compute economics or sales comp
Create foundational datasets that can be used by people and AI tools to answer questions around product use cases, financial reporting, and marketing campaigns
SQL fluency, Python proficiency
Professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, Posthog
Ability to extend their work beyond just data reporting and into action and impact
High attention to detail
Excellent and precise communicator
Strong personability and relationship building skills
Experience with AI products, especially LLM inference and sandboxes
Experience in fin ops, fraud, sales ops, or risk, especially in the context of AI (e.g. token cost optimization)
Project management skills
Strong presence in the data community online and offline
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
Modal considers high-quality documentation to be essential for developer experience, and we see docs becoming even more important as agents increasingly deploy and operate Modal Apps. We are looking for a content-minded engineer who will partner with our product teams to curate Modal’s technical documentation and maintain a high quality bar across multiple dimensions.
Thinking holistically about content architecture and how the docs should evolve as Modal introduces new products and features
Innovating on novel documentation formats and delivery channels to optimize agent productivity, in collaboration with our Agent DX research team
Developing content standards, style guides, and automated enforcement mechanisms to ensure consistent style and high quality
Building and maintaining automated pipelines that will enforce the correctness of code examples and prose descriptions
Collaborating with engineers across the company to author documentation that is clear, consistent, and correct
3+ years of experience in an engineering, product, or developer relations role, ideally in the devtools or infrastructure space
Experience authoring and/or editing high-quality technical content for developers
Knowledge of agentic engineering workflows and best practices
Comfort with modern software development practices (version control, CI/CD, testing)
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role
Modal's LLM inference platform delivers frontier performance for open-source models with best-in-class elasticity and developer experience, made in part possible by our custom runtime with GPU memory snapshots and multi-cloud substrate.
We're looking for a leader to own the direction and execution of this platform to continue to establish us as the clear market leader, working closely with customers like Cognition, Doordash, Ramp, and many more. You'll be leading a group of highly talented engineers working on our market-leading LLM inference offering, spanning the serving stack, routing infrastructure, internal agentic optimization platform, and the user-facing product surface area.
This is a hands-on leadership role — expect to split your time between technical contribution, product shaping and people management depending on what the team needs. You'll set direction, remove blockers, and build a strong engineering culture as your team tackles hard problems in distributed computing, frontier inference serving, and performance optimization.
Responsibilities
Team
Recruit, hire, and grow a high-performing team of engineers; run regular 1:1s focused on coaching, feedback, and career growth.
Set clear performance expectations, hold a high bar, and build an environment where engineers do their best work.
Foster a culture of ownership, accountability, customer obsession, and continuous improvement.
Technical and Product Direction
Drive technical and product decisions through design reviews, code reviews, and architectural discussions.
Lead our efforts working with customers with novel or frontier workloads so they can be successful running on Modal.
Translate learnings from frontier customers into a roadmap for our internal optimization platform and user-facing product, so that gains from optimization and research efforts are accessible to all of our users.
Establish standards for reliability and product excellence; ensure the team owns projects end-to-end, from spec through production.
Cross-Functional Leadership
Partner with business operations and compute strategy on continuing to develop our strategy for compute purchases across a variety of accelerators.
Collaborate with GTM on product launches, positioning, and improving win rates for inference opportunities.
Help guide the roadmap for teams building the infrastructure platform underlying inference, as well as adjacent product teams.
Requirements
10+ years of industry experience, including 3+ years in a leadership role
Track record building high-performance systems at scale
Strong background in cloud infrastructure
Deep knowledge of low-level OS foundations (Linux kernel, file systems, containers, etc.)
Nice to have: Experience working with LLM inference in production and familiarity with underlying concepts like engines, kernels, routing, KV cache management and speculative decoding.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g., Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders
We're hiring the first Account Managers at Modal. You'll report to the Regional Director of Account Management and be a founding member of the team.
This function does not exist yet. There is no playbook, no territory map, no established motion. You'll own a book of business from day one and build the motion at the same time — from fast-moving AI startups to large enterprise teams running critical infrastructure on Modal.
This is a commercial role with a revenue target. You'll be measured on retention and expansion across your accounts. While you won't be delivering the technical recommendations and implementation, the work is technical by nature. Our customers are engineers running GPU workloads, inference, and batch jobs in production, and you need to hold your own in those conversations.
The profile we're hiring is a technical account manager. You've worked at companies that are deeply technical and consumption in nature. You bring the commercial instincts to grow revenue from an existing base and enough technical depth to know which conversation actually matters. And you can credibly work with a CxO all the way down to a Developer.
A founding seat. You're helping build the AM function with us, not joining one that already runs. The account plans, expansion motions, and customer cadence you and the rest of the founding team figure out are what we scale to every AM we hire after you.
A technical customer-facing role. You'll work directly with Developers and ML teams on what they're building and how Modal fits. You translate and triage the deeper technical work to FDEs and Product when it's needed.
A book of business. You’ll own a set of accounts and be responsible for the revenue that comes from them. Expect to be meeting with customers in your first few days because the best playbooks are built from real experiences.
A traditional post-sales role. You're measured on revenue, not health scores.
An on-call support function. You're not fixing bugs or triaging tickets. You're driving growth.
A technical delivery role. You're not implementing for the customer. You know where the technical work goes and you pull in the right people.
An inherited motion. Nothing is handed to you. If you identify gaps in process, tooling, or a playbook — you help build it.
A single-lane job. Our priorities go customer → company → team → me. We focus on delivering maximum impact vs. \"this is my job\".
Build Modal's account management function and iterate on it as we scale the business
Own and deliver on a revenue target across your book of accounts
Grow an account's first use case, expand into a second, and drive standardization across the Modal platform
Get fluent enough in Modal to hold your own with engineering teams on their workloads, and know when to pull in FDEs, Support, or Product
Work with Sales, Solutions Architects, FDEs, Support, and Product to grow accounts and feed customer signal back into the roadmap
Travel to customers as needed to support key accounts and drive expansion opportunities
Customer-facing experience. You've owned a revenue target or supported a revenue target in a highly technical, consumption business.
Technical depth. You work autonomously with Developers through CxOs and understand the core parts of the developer tech stack.
AI builder. You're hands on building with AI tools — not just consuming workflows that were built for you.
Ownership. You run your book of business and projects like you're the CEO of it. You're highly organized bringing stability, predictability, and pace to your work. You take action vs. waiting for direction.
Learning velocity. You operate with the mindset of what I can do today vs. let me pick this up tomorrow. You move fast, take the learnings, and iterate on it.
Cross-functional partner. Low ego, easy to work with, and highly accountable. You ask for feedback, you act on it, and you'd rather be corrected early than right late. Internal teams love working with you.
Experience as a Technical Account Manager, Solutions Engineer, Solutions Consultant, or similar type role
Certification as an engineer (ex. Computer Science Degree, Professional Bootcamps)
Early GTM hire experience at a fast-growing startup
You've sat close to GPU spend, model training, or inference workloads
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll make cold starts feel local when the data is hundreds of milliseconds away, designing the caching, preloading, and peer-to-peer layers that hide object-store latency and keep public ingress off saturated uplinks. You'll own durability and cost at petabyte scale, from streaming and batch replication between origins, to garbage collection over billions of objects. You'll work across the stack, from local disk and page cache to distributed blob storage and garbage collection and you'll help shape what storage becomes next as we push storage closer to workloads.
5+ years of experience writing high-quality production code
Experience building high-performance distributed storage or caching systems at a large scale (the more challenges you've worked through, the better)
Strong cloud skills, including deep familiarity with object storage (S3 or similar), CDNs, and their consistency, throughput, and cost characteristics
Strong knowledge of low-level operating system foundations (Linux kernel, file systems, page cache, containers, etc.)
Willingness to step into the thick of it with our on-call rotation and respond to production incidents
Experience with replication, content addressing, and consistency models in multi-region or multi-cloud systems
Experience operating storage systems at scale (petabyte-scale datasets, high-throughput read/write paths, large-scale garbage collection or data migration)
Experience with data engineering at petabyte-scale.
Prior experience with Rust
P2P sharing of data across workers within a single datacenter to dramatically reduce ingress
Replicating data across multiple blob storage providers
Automating garbage collection across hundreds of petabytes of data
Deploying colocated storage clusters to datacenters to accelerate high-throughput customer workloads
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll automate the integration of new capacity from a growing set of hardware providers; from auditing and benchmarking hosts and clusters, to maintaining our machine images, configuring GPUs, RDMA, networking, and storage, and getting machines into production. You'll build the automation that keeps the fleet healthy without human intervention: detecting bad GPUs, thermals, and disks. You'll dig into whatever is between the hardware and the software that runs on top of it, whether that is a kernel panic, a broadcast storm during boot, or getting our container runtime to run on new architectures and platforms.
5+ years of experience writing high-quality production code
Experience operating fleets of physical hardware (bare metal provisioning, BMC/IPMI, PXE or network boot, firmware) or building the control planes that manage them (the more challenges you've worked through, the better)
Strong cloud skills
Strong knowledge of low-level operating system foundations (Linux kernel, drivers, networking, file systems, containers, etc.)
Effective at debugging across layers, from BGP flapping, Linux RPS, and vBIOS bugs to a Python control-plane service
Willingness to step into the thick of it with our on-call rotation and respond to production incidents
Experience with GPUs and the NVIDIA software stack in production (drivers, health monitoring, XIDs, RDMA/NVLink)
Prior experience with Go
Automatic remediation of unhealthy machines (power cycling, reimaging, GPU recovery) to maximize uptime and minimize operator toil.
Automatic integration of new CPU, GPU, and storage servers into the fleet while managing hardware and network heterogeneity.
Network health monitoring and reliability across many datacenters, and standardization of bare metal network configuration.
Automatic hardware acceptance testing and benchmarking (CPU, disk, GPU, interconnect, network).
Custom network bootloader, machine image pipeline, and kernel and firmware management across the fleet.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll own the full lifecycle of a machine, from accepting and benchmarking new hardware from a growing set of providers, to network bring-up, kernel and image management, GPU and disk health tracking, and automated remediation of unhealthy hosts. You'll manage a team of 3–8 engineers while staying hands-on across the stack which involves BMCs, firmware, PXE, bootloaders, Linux networking, drivers, and distributed control-plane services, and you'll shape our long-term path further down the stack.
7+ years of experience writing high-quality production code
3+ years of direct people management experience, ideally leading a team of engineers through project planning, growth, and performance conversations
Experience operating large fleets of physical hardware at scale (bare metal provisioning, BMC/IPMI, PXE and network boot, firmware) or building the control planes that manage them (the more challenges you've worked through, the better)
Strong cloud skills
Strong knowledge of low-level operating system foundations (Linux kernel, drivers, networking, file systems, containers, etc.)
Experience working with hardware and colocation providers, including hardware acceptance testing and benchmarking
Track record of setting technical direction and driving architectural decisions across a team
Willingness to step into the thick of it with our on-call rotation and respond to production incidents
Experience with GPUs and the NVIDIA software stack (drivers, health monitoring, RDMA/NVLink) in production
Prior experience with Go
Automatic remediation of unhealthy machines (power cycling, reimaging, GPU recovery) to maximize uptime and minimize operator toil.
Automatic integration of new CPU, GPU, and storage servers into the fleet while managing hardware and network heterogeneity.
Network health monitoring and reliability across many datacenters, and standardization of bare metal network configuration.
Automatic hardware acceptance testing and benchmarking (CPU, disk, GPU, interconnect, network).
Custom network bootloader, machine image pipeline, and kernel and firmware management across the fleet.
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll set technical direction for the primitives that other teams (filesystems, training, sandboxes) build on, balancing durability, latency, throughput, and cost. You'll own the roadmap from today's hardest problems (garbage collection at petabyte scale, active-active replication, rate limiting that protects the upstream without wasting utilization) to the architectural bets that decide what blobnet becomes: storage colocated with the GPUs, tiered writes, and capacity planning against provider limits. You'll manage a team of 3–8 engineers while staying hands-on across the stack, from local disk and page cache to distributed blob storage and garbage collection, and you'll guide the observability, automation, and on-call practices that keep the system healthy as it grows by orders of magnitude.
7+ years of experience writing high-quality production code
3+ years of direct people management experience, ideally leading a team of engineers through project planning, growth, and performance conversations
Experience building high-performance distributed storage or caching systems at a large scale (the more challenges you've worked through, the better)
Strong cloud skills, including deep familiarity with object storage (S3 or similar), CDNs, and their consistency, throughput, and cost characteristics
Strong knowledge of low-level operating system foundations (Linux kernel, file systems, page cache, containers, etc.)
Experience with replication, content addressing, and consistency models in multi-region or multi-cloud systems
Experience operating storage systems at scale (petabyte-scale datasets, high-throughput read/write paths, large-scale garbage collection or data migration), including owning cost and capacity planning
Track record of setting technical direction and driving architectural decisions across a team, and of building the primitives other teams depend on
Willingness to step into the thick of it with our on-call rotation and respond to production incidents
Experience with data engineering at petabyte-scale.
Prior experience with Rust
Key Things the Team Is Working On:
P2P sharing of data across workers within a single datacenter to dramatically reduce ingress
Replicating data across multiple blob storage providers
Automating garbage collection across hundreds of petabytes of data
Deploying colocated storage clusters to datacenters to accelerate high-throughput customer workloads