Parasail is building the AI Supercloud, a programmable deployment network that gives AI builders production ready inference endpoints in minutes, scaling to massive traffic without contracts, infrastructure management, or vendor lock in. By aggregating global GPU supply and automating inference optimization, we let companies deploy custom AI models against aggressive latency, throughput, and cost targets, all on simple pay per token economics.
We're one of the fastest moving companies in the second wave of inference providers. Since launching in April 2025, Parasail has grown to process over 500 billion tokens per day at roughly 30% month over month revenue growth, powering AI native teams like Elicit, mem0, Gravity, Kotoba, and Venice. In April 2026 we closed a $32M Series A (co led by Touring Capital and Kindred Ventures, with Samsung NEXT, Flume Ventures, and Banyan Ventures), bringing total funding to $42M. We're using that capital to deepen our orchestration and inference optimization, expand our global compute fabric, and strengthen partnerships across the GPU and data center ecosystem.
It's an inflection moment. Demand for developer controlled AI is exploding, and the customers we support are running some of the most demanding inference workloads in the world. We're looking for engineers who want to sit at that frontier.
We want engineers with real depth in AI/ML and low level systems who see customer support as a product to be engineered, not a queue to be cleared. Think of this less as a support role and more as an engineering seat positioned right at the customer's side.
We're hiring problem solvers, not ticket closers. The issues that reach you are genuinely hard, because they come from production AI workloads operating at the edge of what's possible. You'll sit on the engineering team and commit production code next to the people building the core platform. The one twist is that your priorities are drawn from what the frontier of customer reality teaches you.
In this role you'll:
Write code that compounds. Land bug fixes, new capabilities, and automation that lift the experience for every Parasail customer, not only the person who flagged the problem.
Partner with customers directly. Jump into Slack, email, and live calls to help developers and ML engineers troubleshoot, tune, and design their inference workloads.
Engineers support that scale. Build the dashboards, internal tools, and automated workflows that let a small team deliver great support to a fast growing customer base, and show up sharply at the moments that matter most.
Turn signal into a roadmap. Distill recurring field patterns into specific changes: documentation fixes, API improvements, and proposals for new features.
3+ years of experience in a customer facing technical role.
A real area of strength. You go deep in either low level infrastructure or ML/AI, and you can hold your own in the other.
Systems fundamentals. Comfort across operating systems, file systems, networking, performance profiling, distributed systems, and cluster management.
Hands on AI/ML. You've trained models, squeezed performance out of inference, worked directly with GPUs, or built ML infrastructure yourself.
A bias toward automating. When you hit a manual, repetitive process, your reflex is to engineer it away, and you have the chops to actually do it.
Strong communication. You can walk a customer through a thorny systems issue, file a tight reproducible bug report, and write documentation that lands, all while coordinating internally to get the fix out the door.
Experience supporting or operating a developer facing platform or API.
A track record of customer facing technical work: solutions engineering, forward deployed engineering, or similar.
Familiarity with the modern inference stack (vLLM, TensorRT LLM, CUDA, Kubernetes, or comparable tooling).
Parasail is building the AI Supercloud, a programmable deployment network that gives AI builders production ready inference endpoints in minutes, scaling to massive traffic without contracts, infrastructure management, or vendor lock in. By aggregating global GPU supply and automating inference optimization, we let companies deploy custom AI models against aggressive latency, throughput, and cost targets, all on simple pay per token economics.
We're one of the fastest moving companies in the second wave of inference providers. Since launching in April 2025, Parasail has grown to process over 500 billion tokens per day at roughly 30% month over month revenue growth, powering AI native teams like Elicit, mem0, Gravity, Kotoba, and Venice. In April 2026 we closed a $32M Series A (co led by Touring Capital and Kindred Ventures, with Samsung NEXT, Flume Ventures, and Banyan Ventures), bringing total funding to $42M. We're using that capital to deepen our orchestration and inference optimization, expand our global compute fabric, and strengthen partnerships across the GPU and data center ecosystem.
It's an inflection moment. Demand for developer controlled AI is exploding, and the customers we support are running some of the most demanding inference workloads in the world. We're looking for engineers who want to sit at that frontier.
Sales Engineers are how Parasail earns the trust of the teams building on us. You'll be benchmarking inference, reasoning about latency and cost tradeoffs, and writing real code to get a customer unblocked, not just narrating slides. It's also deeply human: the same week you might pair with a founder's lead engineer in a shared Slack channel and walk a VP through why our numbers hold up. What you learn in those rooms is some of the clearest signal our product team gets, and we expect you to carry it back.
A typical engagement runs through your hands end to end:
Understand the problem. Run deep discovery to fully understand the customer's requirements and use case, what they've optimized for in the past, where they're investing today, and what \"good\" actually looks like to them, before you propose anything.
Prove it on Parasail. Scope a focused proof of concept, agree with the customer on how success will be measured, and drive it to a working result alongside our product and engineering teams. You keep the scope defined and the timeline real.
Tune for production. Run inference sweeps, set performance baselines for their workload, and dial in deployments that hit their specific latency, throughput, and cost numbers. Recommend the right model and serving approach, and help them stand up the evals that keep quality honest once they scale.
Stay their technical partner. Keep the relationship healthy from the first engineer who championed you to the executive who signs off, sustain momentum through longer cycles, and turn what you see in the field, the recurring friction, the missing capability, the pattern across accounts, into concrete input for our roadmap.
A few years of technical, customer facing experience. Roughly 4+ years where you've been the technical owner of customer relationships, in solutions or sales engineering, forward deployed engineering, applied AI, or a comparable hands on role.
Real fluency with inference. You understand serving models at scale and the tradeoffs that come with it, and you can speak credibly about latency, throughput, and cost without hand waving.
You write code. Python specifically, at a level where you can build, read, and debug something that runs in production, not only prototype it.
Cloud at scale. Practical experience operating on AWS, Azure, or GCP and serving real workloads on them.
A communicator people trust. You can lead a focused discovery call, hold your own in front of an executive, and get into the weeds with an engineer, adjusting your altitude to the room.
Hands on time with modern inference tooling such as vLLM, TensorRT LLM, CUDA, or Kubernetes.
A working sense of when and how to fine tune, and the methods behind it.
Experience moving fluidly across engineering, product, and go to market without dropping the thread.
Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads.
This is an experienced individual contributor role and our first dedicated developer-facing hire. You'll write the technical thought leadership content, developer documentation, and performance benchmarks that show how Parasail performs and why it's built the way it is. You'll grow our presence in the communities developers already inhabit and bring what you hear back into engineering, product, and GTM. You'll work alongside our CEO's founder platform, with content and community efforts that amplify each other rather than compete.
Technical content and developer documentation are the core focus. Community presence and events grow from that foundation.
Own developer-facing education and build developer trust. Own developer documentation and educational content, including tutorials, guides, demos, and long-form technical content (deep dives, benchmarks, visual essays) that establishes Parasail as a thought leader in inference. Partner closely with engineering and product on in-product DX and activation.
Grow a thriving developer community. Grow Parasail's developer community through consistent, high-quality touchpoints, including Discord, regular office hours, and an active presence in external communities (r/LocalLLaMA, Hacker News, target external Discords). Synthesize what you hear into a feedback loop that shapes engineering, product, and GTM priorities.
Increase developer brand awareness through industry events. Host and represent Parasail at hackathons, conferences, meetups, and other developer events.
6+ years of combined software engineering, DevRel, and technical content experience; open to exceptional candidates with less experience if your portfolio is unusually strong
Strong technical fluency in inference, model serving, or adjacent infrastructure
Prior experience launching or growing a developer community
You've built AI workflows into your craft and it shows up in what you ship
Comfort working in ambiguity; you'd rather walk in with a thesis and adjust than wait for direction
Relationships with AI builders, founders, and ML practitioners; you're already in the rooms (online and in person) where this audience lives
A signature piece of public work that's traveled: a popular newsletter, a long-form series that gets cited, a book, talks at conferences or meetups, or comparable signal
A track record of going deep on technically demanding problems through ML infra, backend or distributed systems work, open-source contributions, or a portfolio of technical writing, projects, and community work that demonstrates the same dept
Competitive base + meaningful equity
Comprehensive health, dental, and vision coverage
Generous PTO and company holidays
401(k), Family Planning (Fertility)
Opportunity to work at the frontier of AI infrastructure
Parasail is an equal opportunity employer that supports workplace diversity and does not discriminate on the basis of race, color, religion, gender identity/expression, national origin, age, military service eligibility, veteran status, sexual orientation, marital status, physical or mental disability, or any other protected class. Parasail is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities.
","descriptionPlain":"Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads.\n\n\n\n \n\n\nABOUT THE ROLE\n\nThis is an experienced individual contributor role and our first dedicated developer-facing hire. You'll write the technical thought leadership content, developer documentation, and performance benchmarks that show how Parasail performs and why it's built the way it is. You'll grow our presence in the communities developers already inhabit and bring what you hear back into engineering, product, and GTM. You'll work alongside our CEO's founder platform, with content and community efforts that amplify each other rather than compete.\n\n\n\n \n\n\nWHAT YOU'LL DO\n\nTechnical content and developer documentation are the core focus. Community presence and events grow from that foundation.\n\n - Own developer-facing education and build developer trust. Own developer documentation and educational content, including tutorials, guides, demos, and long-form technical content (deep dives, benchmarks, visual essays) that establishes Parasail as a thought leader in inference. Partner closely with engineering and product on in-product DX and activation.\n\n - Grow a thriving developer community. Grow Parasail's developer community through consistent, high-quality touchpoints, including Discord, regular office hours, and an active presence in external communities (r/LocalLLaMA, Hacker News, target external Discords). Synthesize what you hear into a feedback loop that shapes engineering, product, and GTM priorities.\n\n - Increase developer brand awareness through industry events. Host and represent Parasail at hackathons, conferences, meetups, and other developer events.\n \n \n\n\nWHAT YOU’LL BRING\n\n - 6+ years of combined software engineering, DevRel, and technical content experience; open to exceptional candidates with less experience if your portfolio is unusually strong\n\n - Strong technical fluency in inference, model serving, or adjacent infrastructure\n\n - Prior experience launching or growing a developer community\n\n - You've built AI workflows into your craft and it shows up in what you ship\n\n - Comfort working in ambiguity; you'd rather walk in with a thesis and adjust than wait for direction\n\n\n\n\nNICE TO HAVES\n\n - Relationships with AI builders, founders, and ML practitioners; you're already in the rooms (online and in person) where this audience lives\n\n - A signature piece of public work that's traveled: a popular newsletter, a long-form series that gets cited, a book, talks at conferences or meetups, or comparable signal\n\n - A track record of going deep on technically demanding problems through ML infra, backend or distributed systems work, open-source contributions, or a portfolio of technical writing, projects, and community work that demonstrates the same dept\n\n\n\n\nWHAT WE OFFER\n\n - Competitive base + meaningful equity\n\n - Comprehensive health, dental, and vision coverage\n\n - Generous PTO and company holidays\n\n - 401(k), Family Planning (Fertility)\n\n - Opportunity to work at the frontier of AI infrastructure\n\n \n\nParasail is an equal opportunity employer that supports workplace diversity and does not discriminate on the basis of race, color, religion, gender identity/expression, national origin, age, military service eligibility, veteran status, sexual orientation, marital status, physical or mental disability, or any other protected class. Parasail is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities."},{"id":"ed1dc7fe-031e-40be-b61d-7153aa7a094a","title":"Senior Software Engineer, Distributed Systems","department":"Software Engineering","team":"Software Engineering","employmentType":"FullTime","location":"San Mateo","secondaryLocations":[],"publishedAt":"2026-06-22T22:58:21.514+00:00","isListed":true,"isRemote":null,"workplaceType":null,"address":{"postalAddress":{"addressLocality":"San Mateo"}},"jobUrl":"https://jobs.ashbyhq.com/parasail/ed1dc7fe-031e-40be-b61d-7153aa7a094a","applyUrl":"https://jobs.ashbyhq.com/parasail/ed1dc7fe-031e-40be-b61d-7153aa7a094a/application","descriptionHtml":"Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads.
The Senior Software Engineer, Distributed Systems role at Parasail is crucial for ensuring the seamless deployment, scalability, and security of our AI platform. This position is tailored for individuals passionate about creating robust infrastructures that support complex AI workloads, with a strong emphasis on cloud compatibility, security, ease of use, and cost savings. You will work closely with AI-focused performance and solutions engineering teams, facilitating the orchestration of resources needed for high-performing AI applications, all while ensuring our infrastructure adheres to the highest standards of security and data privacy.
Microservice Architecture: Deep understanding of distributed application design patterns, service decomposition, and inter-service communication. Experience in building scalable and maintainable microservice-based systems.
Backend Development: Strong foundation in storage solutions and networking protocols. Ability to design and implement robust backend systems with optimized data management and network communication.
Programming Languages & Frameworks: Extensive experience with Java, Spring Boot, and Golang. Proficiency in developing enterprise-grade applications using modern development frameworks and tools.
Distributed Systems: Deep knowledge of distributed computing principles, including consistency models, fault tolerance, and scalability patterns. Experience in designing and maintaining large-scale distributed applications.
Cloud-Native Technologies: Proficiency in cloud platforms and Infrastructure as Code (IaC). Experience with containerization, orchestration, and automated infrastructure deployment using modern DevOps practices.
Large Language Models: Understanding of LLM technologies and their applications. Experience in integrating and working with AI language models in production environments, including model deployment and optimization.
5+ years of experience in backend or infrastructure, with exposure in Kubernetes and distributed computing environments.
Demonstrated ability to build and secure scalable, high-performance backend.
Hands-on to build brand new system and make end to end work
Excellent problem-solving skills, with a track record of designing solutions that address the complex needs of AI workloads.
While AI experience is not required, a demonstrated capacity to learn new technologies and stay up-to-date on the cutting edge is essential.
This role is pivotal in building a new breed of cloud architecture that enables enterprises to build powerful AI in their preferred environment with the same performance and economics as uncontrolled external services. This role ensures our AI solutions are not just innovative but also secure and sustainable. If you're passionate about learning the cutting edge of AI, building the future of infrastructure, and making a tangible impact to the early days of a company, we're excited to welcome you aboard.
","descriptionPlain":"Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads.\n\n\n\nThe Senior Software Engineer, Distributed Systems role at Parasail is crucial for ensuring the seamless deployment, scalability, and security of our AI platform. This position is tailored for individuals passionate about creating robust infrastructures that support complex AI workloads, with a strong emphasis on cloud compatibility, security, ease of use, and cost savings. You will work closely with AI-focused performance and solutions engineering teams, facilitating the orchestration of resources needed for high-performing AI applications, all while ensuring our infrastructure adheres to the highest standards of security and data privacy.\n\n\n\n\nTECHNICAL ABILITIES\n\n - Microservice Architecture: Deep understanding of distributed application design patterns, service decomposition, and inter-service communication. Experience in building scalable and maintainable microservice-based systems.\n\n - Backend Development: Strong foundation in storage solutions and networking protocols. Ability to design and implement robust backend systems with optimized data management and network communication.\n\n - Programming Languages & Frameworks: Extensive experience with Java, Spring Boot, and Golang. Proficiency in developing enterprise-grade applications using modern development frameworks and tools.\n\n - Distributed Systems: Deep knowledge of distributed computing principles, including consistency models, fault tolerance, and scalability patterns. Experience in designing and maintaining large-scale distributed applications.\n\n - Cloud-Native Technologies: Proficiency in cloud platforms and Infrastructure as Code (IaC). Experience with containerization, orchestration, and automated infrastructure deployment using modern DevOps practices.\n\n - Large Language Models: Understanding of LLM technologies and their applications. Experience in integrating and working with AI language models in production environments, including model deployment and optimization.\n \n \n\n\nQUALIFICATIONS\n\n - 5+ years of experience in backend or infrastructure, with exposure in Kubernetes and distributed computing environments.\n\n - Demonstrated ability to build and secure scalable, high-performance backend.\n\n - Hands-on to build brand new system and make end to end work\n\n - Excellent problem-solving skills, with a track record of designing solutions that address the complex needs of AI workloads.\n\n - While AI experience is not required, a demonstrated capacity to learn new technologies and stay up-to-date on the cutting edge is essential.\n\n\n\n\n\n\n\nWHAT YOU BRING TO THE TABLE\n\nThis role is pivotal in building a new breed of cloud architecture that enables enterprises to build powerful AI in their preferred environment with the same performance and economics as uncontrolled external services. This role ensures our AI solutions are not just innovative but also secure and sustainable. If you're passionate about learning the cutting edge of AI, building the future of infrastructure, and making a tangible impact to the early days of a company, we're excited to welcome you aboard."},{"id":"ffacc083-acca-4917-807c-d20932d111ca","title":"Senior Software Engineer, Full Stack","department":"Software Engineering","team":"Software Engineering","employmentType":"FullTime","location":"San Mateo","secondaryLocations":[],"publishedAt":"2026-06-22T23:02:09.544+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressLocality":"San Mateo"}},"jobUrl":"https://jobs.ashbyhq.com/parasail/ffacc083-acca-4917-807c-d20932d111ca","applyUrl":"https://jobs.ashbyhq.com/parasail/ffacc083-acca-4917-807c-d20932d111ca/application","descriptionHtml":"Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads.
We’re looking for a Senior Fullstack Engineer who wants to own large surface areas of product from database to pixel. You’ll build the interfaces, APIs, and systems that thousands of AI engineers interact with daily — from our real-time inference dashboard and model deployment console to the orchestration APIs that route workloads across a global GPU network.
This isn’t a role where you’ll be handed pixel-perfect mocks and asked to implement them. You’ll shape what we build and how it works, collaborating directly with founders and infrastructure engineers to turn complex distributed systems into products that feel simple and fast.
Inference Platform UI: Design and build the dashboards, model deployment workflows, and real-time monitoring views that our customers use to deploy and manage AI workloads at scale.
APIs & Backend Services: Architect and implement the backend services that power our platform — model serving endpoints, usage metering, billing integrations, authentication, and developer-facing APIs.
Orchestration Interfaces: Build the frontend and API layers on top of our GPU orchestration engine, giving users intuitive control over routing, caching, scaling, and cost optimization across our global network.
Developer Experience: Own the end-to-end developer experience including API documentation, SDK design, CLI tools, and onboarding flows that make Parasail the easiest inference platform to adopt.
Real-Time Systems: Implement streaming interfaces (SSE/WebSockets) for real-time inference responses, live cost tracking, and deployment status updates.
Data & Analytics: Build internal and customer-facing analytics — usage dashboards, cost breakdowns, latency distributions, and performance benchmarking tools.
Frontend Engineering: Strong experience with modern frontend frameworks (React, Next.js, or similar). You write clean, performant TypeScript and care about UI/UX details — loading states, error handling, responsive design, and accessibility.
Backend Development: Deep experience building production backend services in Go, Python, Java, or Node.js. You’re comfortable designing RESTful and gRPC APIs, working with relational and NoSQL databases, and implementing auth, rate limiting, and caching layers.
Distributed Systems Fundamentals: Solid understanding of distributed computing principles — consistency models, fault tolerance, and scalability patterns. You don’t need to be a distributed systems PhD, but you should be comfortable reasoning about systems that span multiple services and regions.
Cloud-Native & Infrastructure: Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines. Bonus if you’ve worked with Infrastructure as Code (Terraform, Pulumi).
Data & Observability: Experience building data pipelines, analytics dashboards, and integrating observability tools (metrics, logging, tracing). You know how to instrument systems so problems surface before customers notice.
AI/ML Familiarity (Nice to Have): Understanding of LLM technologies, inference optimization (batching, caching, quantization), or model serving platforms like vLLM, TGI, or Triton. If you don’t have this yet, you’re excited to learn.
5+ years of professional software engineering experience with significant fullstack ownership (you’ve shipped features end-to-end, from UI to database).
Track record of building and scaling web applications or developer platforms used by technical audiences.
Experience working in fast-paced environments where you’ve had to make pragmatic trade-offs between speed and quality.
Strong product instincts — you think about the user experience, not just the code.
Excellent communication skills. You can explain technical decisions to non-technical stakeholders and write clear documentation.
While deep AI experience is not required, a demonstrated ability to learn new technologies quickly and stay current with the rapidly evolving AI landscape is essential.
Massive impact at an early stage. We’re a small team building infrastructure that powers thousands of AI applications. Your work will be felt by every customer, every day.
Work at the frontier. AI inference is one of the fastest-growing and most technically interesting areas in tech. You’ll work alongside engineers who’ve built AI companies from scratch and shipped products used by industry leaders.
Full ownership, minimal bureaucracy. No PRDs written by someone three levels away. You’ll work directly with founders and ship to production on your own terms.
Competitive compensation. Competitive salary, meaningful equity in a high-growth company, and benefits designed for a distributed team.
The AI inference market is projected to dwarf the training market within a few years. Parasail is building the infrastructure layer that makes this possible. If you want to build products that shape how the world runs AI, we’d love to hear from you.
Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads.
The Senior/Staff Inference Reliability Engineer will own the end-to-end reliability and production performance of customer inference workloads. This role sits at the intersection of inference platform engineering, LLM performance, and infrastructure reliability.
You will ensure that customer endpoints meet expectations for availability, latency, throughput, quality, and cost. When an endpoint degrades, you will follow the problem across the entire serving path—from APIs, routing, scheduling, and autoscaling through model servers, GPUs, networking, and underlying infrastructure—and drive it through resolution.
This is not a traditional DevOps role focused only on clusters and deployments. It is a production systems role for an engineer who enjoys investigating ambiguous performance problems, building diagnostic tooling, and turning recurring incidents into durable platform improvements.
Prior LLM-inference experience is valuable but not required. We are looking for someone with deep production systems experience who can quickly learn inference-specific technologies and metrics.
Own the production health of customer inference workloads, including availability, request success, time to first token, inter-token latency, throughput, and operational efficiency.
Establish clear service-level indicators, objectives, performance baselines, and escalation paths for production endpoints.
Build the telemetry, dashboards, alerts, and automated diagnostics needed to detect meaningful endpoint degradation before customers report it.
Create visibility across the full inference-serving path, including request queues, routing, scheduling, model servers, GPU utilization, networking, storage, and provider infrastructure.
Lead the investigation of complex latency, throughput, capacity, and reliability regressions.
Determine whether an issue originates in customer traffic patterns, platform services, inference-engine configuration, GPU hardware, networking, storage, or an external infrastructure provider.
Remain accountable for the customer outcome while partnering with the appropriate engineering teams to implement the fix.
Help lead customer-impacting incidents and establish effective operational practices for acknowledgement, diagnosis, recovery, and communication.
Convert significant incidents into automated tests, safeguards, runbooks, capacity controls, anomaly detection, and platform improvements.
Partner with the LLM Performance team to validate that engine-level optimizations deliver measurable improvements in production.
Analyze workload behavior, capacity requirements, utilization, tail latency, and cost efficiency across heterogeneous GPU providers and hardware.
Help ensure that customer performance requirements are met without consuming unnecessary infrastructure capacity.
Identify recurring patterns across incidents, workloads, and customer escalations.
Translate those findings into improvements to the inference platform, reliability architecture, deployment processes, observability, and product roadmap.
Deep experience operating critical, customer-facing or business-critical production systems.
Ability to reason about service health across multiple layers rather than treating infrastructure availability as the complete customer outcome.
Experience defining and operating service-level indicators and objectives, building actionable observability, leading incidents, performing failure analysis, and reducing mean time to detection and recovery.
Strong understanding of latency, throughput, queueing, resource contention, capacity, workload distribution, and tail-performance behavior.
Demonstrated ability to diagnose difficult production regressions and isolate bottlenecks across applications and infrastructure.
Knowledge of distributed-systems principles, including fault tolerance, scheduling, routing, load balancing, capacity management, consistency, and failure recovery.
Strong experience with Kubernetes, Linux, networking, storage, cloud infrastructure, and containerized production environments.
Experience operating across multiple cloud providers, regions, hardware configurations, or infrastructure suppliers is especially valuable.
Ability to write production-quality software and build internal tooling, instrumentation, automation, and diagnostic systems.
Proficiency in languages such as Python, Go, Java, C++, or Rust.
Experience with GPUs, ML infrastructure, model serving, vLLM, SGLang, Triton, TensorRT-LLM, or similar technologies is valuable but not required.
You should be excited to develop expertise in concepts such as time to first token, inter-token latency, continuous batching, KV caching, speculative decoding, quantization, and tokens per GPU.
5+ years of experience in production engineering, site reliability engineering, infrastructure engineering, distributed systems, ML infrastructure, database reliability, or performance engineering.
Demonstrated ownership of a critical production service or workload.
Experience diagnosing complex latency, throughput, capacity, or reliability problems across multiple system layers.
Strong software-engineering ability beyond infrastructure configuration and CI/CD automation.
Hands-on experience building observability, automation, diagnostic tooling, or production safeguards.
Strong communication and technical leadership skills, including the ability to coordinate incident resolution across engineering teams.
Experience with Kubernetes, Linux, networking, and cloud-native infrastructure.
While prior LLM-inference experience is not required, a demonstrated ability to learn unfamiliar systems and develop deep technical expertise is essential.
Within your first six months, you will help establish clear end-to-end ownership and observability for Parasail’s production inference workloads.
Success means:
Material endpoint regressions are increasingly detected before customers report them.
Engineers can quickly determine which layer of the system is responsible for a production issue.
Customer-impacting incidents are acknowledged, diagnosed, and resolved faster.
Production endpoints consistently meet defined availability, latency, throughput, and efficiency objectives.
Recurring failure modes are converted into automated detection, safeguards, and durable platform improvements.
Infrastructure and inference capacity are used efficiently while preserving customer performance and model quality.
This role is pivotal in building a new operational discipline for AI infrastructure.
You may come from GPU infrastructure, ML platforms, databases, streaming systems, search, low-latency services, distributed storage, or large-scale production engineering. You do not need to have previously held the title “Inference Reliability Engineer.”
What matters is that you have owned important production systems, can investigate problems that span organizational and technical boundaries, and continue following an issue until the customer experience is restored.
If you are passionate about production systems, performance, reliability, and learning the cutting edge of AI infrastructure, we are excited to welcome you aboard.
","descriptionPlain":"Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads.\n\nThe Senior/Staff Inference Reliability Engineer will own the end-to-end reliability and production performance of customer inference workloads. This role sits at the intersection of inference platform engineering, LLM performance, and infrastructure reliability.\n\nYou will ensure that customer endpoints meet expectations for availability, latency, throughput, quality, and cost. When an endpoint degrades, you will follow the problem across the entire serving path—from APIs, routing, scheduling, and autoscaling through model servers, GPUs, networking, and underlying infrastructure—and drive it through resolution.\n\nThis is not a traditional DevOps role focused only on clusters and deployments. It is a production systems role for an engineer who enjoys investigating ambiguous performance problems, building diagnostic tooling, and turning recurring incidents into durable platform improvements.\n\nPrior LLM-inference experience is valuable but not required. We are looking for someone with deep production systems experience who can quickly learn inference-specific technologies and metrics.\n\n\n\n\nWHAT YOU WILL OWN\n\n\nEND-TO-END INFERENCE RELIABILITY\n\nOwn the production health of customer inference workloads, including availability, request success, time to first token, inter-token latency, throughput, and operational efficiency.\n\nEstablish clear service-level indicators, objectives, performance baselines, and escalation paths for production endpoints.\n\n\nDETECTION AND OBSERVABILITY\n\nBuild the telemetry, dashboards, alerts, and automated diagnostics needed to detect meaningful endpoint degradation before customers report it.\n\nCreate visibility across the full inference-serving path, including request queues, routing, scheduling, model servers, GPU utilization, networking, storage, and provider infrastructure.\n\n\nPRODUCTION INVESTIGATION\n\nLead the investigation of complex latency, throughput, capacity, and reliability regressions.\n\nDetermine whether an issue originates in customer traffic patterns, platform services, inference-engine configuration, GPU hardware, networking, storage, or an external infrastructure provider.\n\nRemain accountable for the customer outcome while partnering with the appropriate engineering teams to implement the fix.\n\n\nINCIDENT RESPONSE AND PREVENTION\n\nHelp lead customer-impacting incidents and establish effective operational practices for acknowledgement, diagnosis, recovery, and communication.\n\nConvert significant incidents into automated tests, safeguards, runbooks, capacity controls, anomaly detection, and platform improvements.\n\n\nPERFORMANCE AND CAPACITY\n\nPartner with the LLM Performance team to validate that engine-level optimizations deliver measurable improvements in production.\n\nAnalyze workload behavior, capacity requirements, utilization, tail latency, and cost efficiency across heterogeneous GPU providers and hardware.\n\nHelp ensure that customer performance requirements are met without consuming unnecessary infrastructure capacity.\n\n\nPRODUCTION FEEDBACK LOOP\n\nIdentify recurring patterns across incidents, workloads, and customer escalations.\n\nTranslate those findings into improvements to the inference platform, reliability architecture, deployment processes, observability, and product roadmap.\n\n\n\n\nTECHNICAL ABILITIES\n\n\nPRODUCTION SYSTEMS\n\nDeep experience operating critical, customer-facing or business-critical production systems.\n\nAbility to reason about service health across multiple layers rather than treating infrastructure availability as the complete customer outcome.\n\n\nRELIABILITY ENGINEERING\n\nExperience defining and operating service-level indicators and objectives, building actionable observability, leading incidents, performing failure analysis, and reducing mean time to detection and recovery.\n\n\nPERFORMANCE DIAGNOSIS\n\nStrong understanding of latency, throughput, queueing, resource contention, capacity, workload distribution, and tail-performance behavior.\n\nDemonstrated ability to diagnose difficult production regressions and isolate bottlenecks across applications and infrastructure.\n\n\nDISTRIBUTED SYSTEMS\n\nKnowledge of distributed-systems principles, including fault tolerance, scheduling, routing, load balancing, capacity management, consistency, and failure recovery.\n\n\nCLOUD AND INFRASTRUCTURE\n\nStrong experience with Kubernetes, Linux, networking, storage, cloud infrastructure, and containerized production environments.\n\nExperience operating across multiple cloud providers, regions, hardware configurations, or infrastructure suppliers is especially valuable.\n\n\nSOFTWARE ENGINEERING\n\nAbility to write production-quality software and build internal tooling, instrumentation, automation, and diagnostic systems.\n\nProficiency in languages such as Python, Go, Java, C++, or Rust.\n\n\nAI AND INFERENCE SYSTEMS\n\nExperience with GPUs, ML infrastructure, model serving, vLLM, SGLang, Triton, TensorRT-LLM, or similar technologies is valuable but not required.\n\nYou should be excited to develop expertise in concepts such as time to first token, inter-token latency, continuous batching, KV caching, speculative decoding, quantization, and tokens per GPU.\n\n\n\n\nQUALIFICATIONS\n\n5+ years of experience in production engineering, site reliability engineering, infrastructure engineering, distributed systems, ML infrastructure, database reliability, or performance engineering.\n\nDemonstrated ownership of a critical production service or workload.\n\nExperience diagnosing complex latency, throughput, capacity, or reliability problems across multiple system layers.\n\nStrong software-engineering ability beyond infrastructure configuration and CI/CD automation.\n\nHands-on experience building observability, automation, diagnostic tooling, or production safeguards.\n\nStrong communication and technical leadership skills, including the ability to coordinate incident resolution across engineering teams.\n\nExperience with Kubernetes, Linux, networking, and cloud-native infrastructure.\n\nWhile prior LLM-inference experience is not required, a demonstrated ability to learn unfamiliar systems and develop deep technical expertise is essential.\n\n\n\n\nWHAT SUCCESS LOOKS LIKE\n\nWithin your first six months, you will help establish clear end-to-end ownership and observability for Parasail’s production inference workloads.\n\nSuccess means:\n\n - Material endpoint regressions are increasingly detected before customers report them.\n\n - Engineers can quickly determine which layer of the system is responsible for a production issue.\n\n - Customer-impacting incidents are acknowledged, diagnosed, and resolved faster.\n\n - Production endpoints consistently meet defined availability, latency, throughput, and efficiency objectives.\n\n - Recurring failure modes are converted into automated detection, safeguards, and durable platform improvements.\n\n - Infrastructure and inference capacity are used efficiently while preserving customer performance and model quality.\n\n\n\n\nWHAT YOU BRING TO THE TABLE\n\nThis role is pivotal in building a new operational discipline for AI infrastructure.\n\nYou may come from GPU infrastructure, ML platforms, databases, streaming systems, search, low-latency services, distributed storage, or large-scale production engineering. You do not need to have previously held the title “Inference Reliability Engineer.”\n\nWhat matters is that you have owned important production systems, can investigate problems that span organizational and technical boundaries, and continue following an issue until the customer experience is restored.\n\nIf you are passionate about production systems, performance, reliability, and learning the cutting edge of AI infrastructure, we are excited to welcome you aboard."},{"id":"b87744b3-4f4e-4345-ade2-979cf09436d7","title":"Strategic Finance Associate","department":"Operations","team":"Operations","employmentType":"FullTime","location":"San Francisco / San Mateo","secondaryLocations":[],"publishedAt":"2026-08-03T18:48:00.267+00:00","isListed":true,"isRemote":null,"workplaceType":null,"address":null,"jobUrl":"https://jobs.ashbyhq.com/parasail/b87744b3-4f4e-4345-ade2-979cf09436d7","applyUrl":"https://jobs.ashbyhq.com/parasail/b87744b3-4f4e-4345-ade2-979cf09436d7/application","descriptionHtml":"Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience, free from vendor lock-in and designed for the next generation of AI workloads.
We're looking for a Strategic Finance Associate to help shape the commercial and capital decisions behind Parasail's growth.
You'll build the financial models that guide customer contracts, GPU investments, pricing, and fleet strategy. Our models are live systems that pull from our own infrastructure, billing, and CRM data through MCP connections, built and maintained inside agentic tools like Claude Code. You don't need to be an engineer, but you should already work this way and be productive in our stack from week one.
Working closely with finance leadership and partners across sales, engineering, and operations, you'll connect how the platform runs, including capacity, utilization, and performance, to revenue, margins, and returns on invested capital. This is a highly visible role with meaningful ownership from day one. Your analysis will directly inform negotiations, resource allocation, and strategic decisions in one of technology's fastest-growing and most capital-intensive markets.
Evaluate customer contracts and commercial opportunities. Build and maintain financial models that assess deal structures, margins, payback periods, and risk. Translate your analysis into clear recommendations that support commercial negotiations.
Underwrite GPU investments. Model GPU purchase decisions, including lease-versus-buy scenarios, financing structures, utilization assumptions, useful life, residual value, and return on invested capital.
Shape pricing strategy. Analyze unit economics across products, workloads, and hardware types. Benchmark Parasail's offerings against the market and model the revenue, margin, and customer implications of pricing changes.
Improve fleet economics. Track utilization, cost, and profitability by GPU type and data center. Identify opportunities to improve deployment decisions, capacity allocation, and fleet-level returns.
Build the systems behind the numbers. Use AI tooling to stand up the models, queries, and internal tools that turn our usage, billing, and infrastructure data into financial output, and automate the recurring work worth eliminating.
Connect operating performance to financial outcomes. Develop dashboards and recurring reporting that link operational metrics, including capacity, utilization, and tokens served, to revenue, margins, cash flow, and capital efficiency.
Support planning and strategic decisions. Contribute to forecasting, budgeting, investor reporting, and high-priority analyses for finance leadership and the executive team.
Work across the business. Partner closely with sales, engineering, and operations to ensure your models reflect how the business actually operates and can be used to make real-world decisions.
2 to 6 years of experience in investment banking, management consulting, private equity, strategic finance, or FP&A at a high-growth technology company
Daily, proactive use of agentic AI tools such as Claude Code, including working with MCP connections to pull data from source systems. You should be able to walk us through models, systems, or workflows you've built this way.
Exceptional financial modeling skills, with the ability to build clean, flexible, assumption-driven models from a blank sheet and defend the reasoning behind every number
Strong analytical judgment and structured problem-solving skills, including comfort working with ambiguity and imperfect data
The ability to distill complex analysis into concise, actionable recommendations for executives and cross-functional partners
A high degree of ownership, initiative, and attention to detail
The ability to move quickly and operate effectively in a fast-paced startup environment
Genuine interest in AI infrastructure, cloud economics, or capital-intensive business models
Experience with cloud infrastructure, data centers, GPUs, semiconductors, or another hardware-intensive business
Familiarity with capital-equipment financing, asset-backed structures, or lease accounting
Working knowledge of SQL or Python for data analysis
Experience with usage-based or consumption-based pricing models
Experience building internal tools or automations outside your formal job description
Competitive base compensation and meaningful equity
Comprehensive health, dental, and vision coverage
Generous PTO and company holidays
401(k) and family-planning benefits
Direct mentorship from and daily collaboration with finance leadership
High visibility with Parasail's executive team
Meaningful ownership over decisions involving real capital and commercial outcomes
The opportunity to help build the financial foundation of a company operating at the frontier of AI infrastructure
Parasail is an equal opportunity employer that values workplace diversity and does not discriminate on the basis of race, color, religion, gender, gender identity or expression, national origin, age, military or veteran status, sexual orientation, marital status, physical or mental disability, or any other status protected by applicable law. Parasail is committed to providing reasonable accommodations to qualified applicants and employees with disabilities.
","descriptionPlain":"STRATEGIC FINANCE ASSOCIATE\n\nParasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience, free from vendor lock-in and designed for the next generation of AI workloads.\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Strategic Finance Associate to help shape the commercial and capital decisions behind Parasail's growth.\nYou'll build the financial models that guide customer contracts, GPU investments, pricing, and fleet strategy. Our models are live systems that pull from our own infrastructure, billing, and CRM data through MCP connections, built and maintained inside agentic tools like Claude Code. You don't need to be an engineer, but you should already work this way and be productive in our stack from week one.\nWorking closely with finance leadership and partners across sales, engineering, and operations, you'll connect how the platform runs, including capacity, utilization, and performance, to revenue, margins, and returns on invested capital. This is a highly visible role with meaningful ownership from day one. Your analysis will directly inform negotiations, resource allocation, and strategic decisions in one of technology's fastest-growing and most capital-intensive markets.\n\n\n\nWHAT YOU'LL DO\n\nEvaluate customer contracts and commercial opportunities. Build and maintain financial models that assess deal structures, margins, payback periods, and risk. Translate your analysis into clear recommendations that support commercial negotiations.\nUnderwrite GPU investments. Model GPU purchase decisions, including lease-versus-buy scenarios, financing structures, utilization assumptions, useful life, residual value, and return on invested capital.\nShape pricing strategy. Analyze unit economics across products, workloads, and hardware types. Benchmark Parasail's offerings against the market and model the revenue, margin, and customer implications of pricing changes.\nImprove fleet economics. Track utilization, cost, and profitability by GPU type and data center. Identify opportunities to improve deployment decisions, capacity allocation, and fleet-level returns.\nBuild the systems behind the numbers. Use AI tooling to stand up the models, queries, and internal tools that turn our usage, billing, and infrastructure data into financial output, and automate the recurring work worth eliminating.\nConnect operating performance to financial outcomes. Develop dashboards and recurring reporting that link operational metrics, including capacity, utilization, and tokens served, to revenue, margins, cash flow, and capital efficiency.\nSupport planning and strategic decisions. Contribute to forecasting, budgeting, investor reporting, and high-priority analyses for finance leadership and the executive team.\nWork across the business. Partner closely with sales, engineering, and operations to ensure your models reflect how the business actually operates and can be used to make real-world decisions.\n\n\n\nWHAT YOU'LL BRING\n\n - 2 to 6 years of experience in investment banking, management consulting, private equity, strategic finance, or FP&A at a high-growth technology company\n\n - Daily, proactive use of agentic AI tools such as Claude Code, including working with MCP connections to pull data from source systems. You should be able to walk us through models, systems, or workflows you've built this way.\n\n - Exceptional financial modeling skills, with the ability to build clean, flexible, assumption-driven models from a blank sheet and defend the reasoning behind every number\n\n - Strong analytical judgment and structured problem-solving skills, including comfort working with ambiguity and imperfect data\n\n - The ability to distill complex analysis into concise, actionable recommendations for executives and cross-functional partners\n\n - A high degree of ownership, initiative, and attention to detail\n\n - The ability to move quickly and operate effectively in a fast-paced startup environment\n\n - Genuine interest in AI infrastructure, cloud economics, or capital-intensive business models\n \n\n\nNICE TO HAVE\n\n - Experience with cloud infrastructure, data centers, GPUs, semiconductors, or another hardware-intensive business\n\n - Familiarity with capital-equipment financing, asset-backed structures, or lease accounting\n\n - Working knowledge of SQL or Python for data analysis\n\n - Experience with usage-based or consumption-based pricing models\n\n - Experience building internal tools or automations outside your formal job description\n\n\n\n\nWHAT WE OFFER\n\n - Competitive base compensation and meaningful equity\n\n - Comprehensive health, dental, and vision coverage\n\n - Generous PTO and company holidays\n\n - 401(k) and family-planning benefits\n\n - Direct mentorship from and daily collaboration with finance leadership\n\n - High visibility with Parasail's executive team\n\n - Meaningful ownership over decisions involving real capital and commercial outcomes\n\n - The opportunity to help build the financial foundation of a company operating at the frontier of AI infrastructure\n\nParasail is an equal opportunity employer that values workplace diversity and does not discriminate on the basis of race, color, religion, gender, gender identity or expression, national origin, age, military or veteran status, sexual orientation, marital status, physical or mental disability, or any other status protected by applicable law. Parasail is committed to providing reasonable accommodations to qualified applicants and employees with disabilities."}],"apiVersion":"1"}