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":"ebc2a015-e445-4b42-a125-207e6e0fcfc2","title":"Business Intelligence Associate","department":"Operations","team":"Operations","employmentType":"FullTime","location":"San Francisco / San Mateo","secondaryLocations":[],"publishedAt":"2026-08-10T19:57:43.198+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":null,"jobUrl":"https://jobs.ashbyhq.com/parasail/ebc2a015-e445-4b42-a125-207e6e0fcfc2","applyUrl":"https://jobs.ashbyhq.com/parasail/ebc2a015-e445-4b42-a125-207e6e0fcfc2/application","descriptionHtml":"Parasail is redefining AI infrastructure by enabling seamless deployment across distributed GPU networks, optimizing for cost, performance, and flexibility. Our mission is to give AI developers a fast, cost-efficient, scalable cloud experience free from vendor lock-in.
We build the company the same way we build the product. Parasail runs on agents, from kernel optimization through to how we answer questions about our own business, and this role sits close to the center of that.
Every inference request we serve is a decision about hardware, cost, and performance. Making those decisions well, at scale, across a distributed GPU fleet, is a data problem before it is anything else. The intelligence layer behind those decisions is one of Parasail's real advantages, and it is what this role owns.
Our internal analytics are agent-native. Teams ask the business questions directly and get real analysis in minutes, on data infrastructure designed so that people and agents query it the same way. It is a different way to run a company, and it is a large part of why a small team moves as fast as we do.
You will own and expand that layer. You will design the data models behind fleet and unit economics, build the analytical capabilities the rest of the company runs on, and turn questions that used to take a week into answers that take a minute. Work you ship compounds, because it becomes something the entire team can use immediately.
You will work through MCP connections and agentic tools every day. We expect you to be productive in that stack from week one.
What does a token actually cost us across hardware generations and workload profiles, and where does that curve bend?
Which workloads run best on which hardware, and what is that difference worth to a customer?
Where is capacity earning, and where is it waiting, across every GPU and every region we operate in?
How does a model's benchmark performance translate into real unit economics at production scale?
As the fleet gets more efficient, how much of that can we hand back to customers as better pricing?
Design and own the data models behind fleet economics, capacity, and utilization
Build the analytical capabilities and reporting the company makes decisions from, and expand what our internal agentic tooling can answer
Build and maintain the pipelines that bring infrastructure, usage, billing, and customer data into one coherent view
Develop dashboards and models connecting operational performance to revenue, margin, and capital efficiency
Build verification into the data layer so answers stay trustworthy as the product evolves quickly
Partner with infrastructure, finance, product, and go-to-market to anticipate what they need to know
Use agentic tooling to automate analysis and reporting, and raise the ceiling on what one person can cover
2 to 5 years in business intelligence, data analytics, analytics engineering, operations analytics, or a similar hands-on data role
Strong SQL, plus Python or a comparable language for data work
Daily proactive use of agentic AI tools with MCP connections to pull data from source systems
Real curiosity about how a business works underneath the metrics, and the drive to chase a number until you understand it completely
Technically literate enough to work directly with infrastructure and engineering teams and reason about what the systems are doing
Ability to explain what the data says, and how confident you are in it, to any audience
High ownership and strong instincts for what is worth measuring
Excited by a fast-moving startup where you get to define your own scope
Experience in operations, manufacturing, logistics, or another environment where asset utilization and yield drive the business
Cloud infrastructure, data center, GPU, or semiconductor exposure
Usage-based or consumption-based billing experience
Experience building internal tools, pipelines, or automation
Familiarity with dbt, warehouse modeling, or observability tooling
Competitive base salary with meaningful equity
Comprehensive health, dental, and vision coverage
Generous PTO and company holidays
401(k) and family-planning benefits
High visibility with the executive team and direct exposure to how the business is run
The chance to build agentic data infrastructure that the entire company runs on
Parasail is an equal opportunity employer committed to workplace diversity and reasonable accommodations for qualified applicants with disabilities.
","descriptionPlain":"COMPANY OVERVIEW\n\nParasail is redefining AI infrastructure by enabling seamless deployment across distributed GPU networks, optimizing for cost, performance, and flexibility. Our mission is to give AI developers a fast, cost-efficient, scalable cloud experience free from vendor lock-in.\n\nWe build the company the same way we build the product. Parasail runs on agents, from kernel optimization through to how we answer questions about our own business, and this role sits close to the center of that.\n\n\n\n\nROLE SUMMARY\n\nEvery inference request we serve is a decision about hardware, cost, and performance. Making those decisions well, at scale, across a distributed GPU fleet, is a data problem before it is anything else. The intelligence layer behind those decisions is one of Parasail's real advantages, and it is what this role owns.\n\nOur internal analytics are agent-native. Teams ask the business questions directly and get real analysis in minutes, on data infrastructure designed so that people and agents query it the same way. It is a different way to run a company, and it is a large part of why a small team moves as fast as we do.\n\nYou will own and expand that layer. You will design the data models behind fleet and unit economics, build the analytical capabilities the rest of the company runs on, and turn questions that used to take a week into answers that take a minute. Work you ship compounds, because it becomes something the entire team can use immediately.\n\nYou will work through MCP connections and agentic tools every day. We expect you to be productive in that stack from week one.\n\n\n\n\nQUESTIONS YOU WILL ANSWER\n\n - What does a token actually cost us across hardware generations and workload profiles, and where does that curve bend?\n\n - Which workloads run best on which hardware, and what is that difference worth to a customer?\n\n - Where is capacity earning, and where is it waiting, across every GPU and every region we operate in?\n\n - How does a model's benchmark performance translate into real unit economics at production scale?\n\n - As the fleet gets more efficient, how much of that can we hand back to customers as better pricing?\n\n\nKEY RESPONSIBILITIES\n\n - Design and own the data models behind fleet economics, capacity, and utilization\n\n - Build the analytical capabilities and reporting the company makes decisions from, and expand what our internal agentic tooling can answer\n\n - Build and maintain the pipelines that bring infrastructure, usage, billing, and customer data into one coherent view\n\n - Develop dashboards and models connecting operational performance to revenue, margin, and capital efficiency\n\n - Build verification into the data layer so answers stay trustworthy as the product evolves quickly\n\n - Partner with infrastructure, finance, product, and go-to-market to anticipate what they need to know\n\n - Use agentic tooling to automate analysis and reporting, and raise the ceiling on what one person can cover\n\n\nREQUIRED QUALIFICATIONS\n\n - 2 to 5 years in business intelligence, data analytics, analytics engineering, operations analytics, or a similar hands-on data role\n\n - Strong SQL, plus Python or a comparable language for data work\n\n - Daily proactive use of agentic AI tools with MCP connections to pull data from source systems\n\n - Real curiosity about how a business works underneath the metrics, and the drive to chase a number until you understand it completely\n\n - Technically literate enough to work directly with infrastructure and engineering teams and reason about what the systems are doing\n\n - Ability to explain what the data says, and how confident you are in it, to any audience\n\n - High ownership and strong instincts for what is worth measuring\n\n - Excited by a fast-moving startup where you get to define your own scope\n\n\nNICE TO HAVE\n\n - Experience in operations, manufacturing, logistics, or another environment where asset utilization and yield drive the business\n\n - Cloud infrastructure, data center, GPU, or semiconductor exposure\n\n - Usage-based or consumption-based billing experience\n\n - Experience building internal tools, pipelines, or automation\n\n - Familiarity with dbt, warehouse modeling, or observability tooling\n\n\nCOMPENSATION & BENEFITS\n\n - Competitive base salary with 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 - High visibility with the executive team and direct exposure to how the business is run\n\n - The chance to build agentic data infrastructure that the entire company runs on\n\nParasail is an equal opportunity employer committed to workplace diversity and reasonable accommodations for qualified applicants with disabilities."},{"id":"e72a92d3-cd1c-4d61-a1ab-93f6a9461c96","title":"Technical Program Manager, Data Center Deployment & Operations","department":"DC Ops","team":"DC Ops","employmentType":"FullTime","location":"San Francisco / San Mateo","secondaryLocations":[],"publishedAt":"2026-09-11T22:24:00.153+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":null,"jobUrl":"https://jobs.ashbyhq.com/parasail/e72a92d3-cd1c-4d61-a1ab-93f6a9461c96","applyUrl":"https://jobs.ashbyhq.com/parasail/e72a92d3-cd1c-4d61-a1ab-93f6a9461c96/application","descriptionHtml":"Parasail builds inference infrastructure for open-weight AI models. Behind one API, we bring together distributed GPU capacity and optimize how workloads run across it, helping customers access reliable, high-performance inference without managing the underlying infrastructure.
We’re building the team that deploys and operates the hardware behind that platform.
You’ll own the delivery and ongoing operation of Parasail’s GPU infrastructure within partner data centers—from white space fit-out and site readiness through equipment installation, validation, and production launch.
Initially, data center partners, hardware vendors, installation contractors, and remote-hands providers will perform much of the physical work. You’ll own those operational relationships and be accountable for execution: setting expectations, coordinating dependencies, verifying quality, and resolving issues.
You’ll work closely with engineering and supply chain to align technical requirements, equipment availability, and site readiness. After launch, you’ll oversee the partners and processes that keep the infrastructure running reliably. As Parasail grows, you’ll help determine which capabilities to bring in-house and build the team to support deployments and operations across locations.
Own partner and vendor execution. Manage operational relationships with data center providers, installation contractors, and remote-hands teams. Define scopes of work, responsibilities, service expectations, and escalation paths.
Lead white space fit-out and deployment. Coordinate rack layouts, power and cooling readiness, structured cabling, equipment installation, and network connectivity with site partners and engineering.
Run the integrated delivery plan. Connect hardware delivery, site preparation, installation, testing, and production readiness into one schedule. Track dependencies, hold owners accountable, and resolve blockers.
Translate requirements into executable work. Ensure engineering specifications become clear vendor deliverables. Review plans with technical owners and manage changes before they create rework or delays.
Drive validation and acceptance. Establish completion criteria with engineering, coordinate hardware checks and burn-in, and verify installation quality and documentation before accepting work.
Own ongoing physical infrastructure operations. Coordinate maintenance, hardware replacements, site access, and changes with vendors and engineering. Establish support coverage and procedures that protect service availability.
Lead operational escalations. Bring together vendors, site teams, and engineers to resolve hardware and site issues. Track restoration, communicate impact, and ensure corrective actions are completed.
Maintain accurate operational records. Ensure rack layouts, asset locations, configurations, and service histories stay current. Partner with Supply Chain on receiving discrepancies, spares, and returns.
Manage delivery costs and risks. Track vendor work against agreed scope and budget, review change requests, and communicate schedule or service risks with clear recovery options.
Build the function. Develop repeatable deployment and operating practices. Identify staffing needs, help hire and develop the internal team, and define how employees and external partners work together as Parasail scales.
Experience delivering and supporting physical infrastructure in data center environments, including white space fit-out, hardware deployment, or technical operations.
Direct responsibility for managing vendors or service providers and holding them accountable for schedule, quality, and service performance.
Experience taking deployments from requirements and planning through installation, validation, and operational handoff.
Working technical knowledge of server hardware, rack infrastructure, cabling, networking, and power and cooling requirements sufficient to evaluate plans and assess completed work.
Strong program management skills, including coordinating multiple workstreams, managing dependencies, and driving issues to closure.
Experience coordinating maintenance or incident response in production environments.
The ability to build practical processes where ownership and systems are still developing.
Clear communication with technical teams, external partners, and leadership, especially when a deployment or service commitment is at risk.
Willingness to travel for site assessments, deployments, partner reviews, and operational needs.
GPU infrastructure, high-density racks, liquid cooling, or high-speed network fabrics.
Managing colocation providers, installation contractors, or remote-hands services across multiple sites.
Earlier hands-on experience installing, troubleshooting, or maintaining data center equipment.
Defining vendor scopes of work, acceptance criteria, and service-level expectations.
Hiring, mentoring, or leading data center technicians or infrastructure operations teams.
Building an internal operations capability alongside external service providers.
GPU capacity enters production on a dependable schedule, with installation quality and operational readiness verified. Partners understand their responsibilities and meet their commitments. Hardware issues are resolved promptly, records remain accurate, and each deployment strengthens the processes and team supporting the next.
","descriptionPlain":"TECHNICAL PROGRAM MANAGER, DATA CENTER DEPLOYMENT & OPERATIONS\n\n\n\n\nABOUT PARASAIL\n\nParasail builds inference infrastructure for open-weight AI models. Behind one API, we bring together distributed GPU capacity and optimize how workloads run across it, helping customers access reliable, high-performance inference without managing the underlying infrastructure.\n\nWe’re building the team that deploys and operates the hardware behind that platform.\n\n\nABOUT THE ROLE\n\nYou’ll own the delivery and ongoing operation of Parasail’s GPU infrastructure within partner data centers—from white space fit-out and site readiness through equipment installation, validation, and production launch.\n\nInitially, data center partners, hardware vendors, installation contractors, and remote-hands providers will perform much of the physical work. You’ll own those operational relationships and be accountable for execution: setting expectations, coordinating dependencies, verifying quality, and resolving issues.\n\nYou’ll work closely with engineering and supply chain to align technical requirements, equipment availability, and site readiness. After launch, you’ll oversee the partners and processes that keep the infrastructure running reliably. As Parasail grows, you’ll help determine which capabilities to bring in-house and build the team to support deployments and operations across locations.\n\n\nWHAT YOU’LL DO\n\n - Own partner and vendor execution. Manage operational relationships with data center providers, installation contractors, and remote-hands teams. Define scopes of work, responsibilities, service expectations, and escalation paths.\n\n - Lead white space fit-out and deployment. Coordinate rack layouts, power and cooling readiness, structured cabling, equipment installation, and network connectivity with site partners and engineering.\n\n - Run the integrated delivery plan. Connect hardware delivery, site preparation, installation, testing, and production readiness into one schedule. Track dependencies, hold owners accountable, and resolve blockers.\n\n - Translate requirements into executable work. Ensure engineering specifications become clear vendor deliverables. Review plans with technical owners and manage changes before they create rework or delays.\n\n - Drive validation and acceptance. Establish completion criteria with engineering, coordinate hardware checks and burn-in, and verify installation quality and documentation before accepting work.\n\n - Own ongoing physical infrastructure operations. Coordinate maintenance, hardware replacements, site access, and changes with vendors and engineering. Establish support coverage and procedures that protect service availability.\n\n - Lead operational escalations. Bring together vendors, site teams, and engineers to resolve hardware and site issues. Track restoration, communicate impact, and ensure corrective actions are completed.\n\n - Maintain accurate operational records. Ensure rack layouts, asset locations, configurations, and service histories stay current. Partner with Supply Chain on receiving discrepancies, spares, and returns.\n\n - Manage delivery costs and risks. Track vendor work against agreed scope and budget, review change requests, and communicate schedule or service risks with clear recovery options.\n\n - Build the function. Develop repeatable deployment and operating practices. Identify staffing needs, help hire and develop the internal team, and define how employees and external partners work together as Parasail scales.\n\n\nWHAT YOU BRING\n\n - Experience delivering and supporting physical infrastructure in data center environments, including white space fit-out, hardware deployment, or technical operations.\n\n - Direct responsibility for managing vendors or service providers and holding them accountable for schedule, quality, and service performance.\n\n - Experience taking deployments from requirements and planning through installation, validation, and operational handoff.\n\n - Working technical knowledge of server hardware, rack infrastructure, cabling, networking, and power and cooling requirements sufficient to evaluate plans and assess completed work.\n\n - Strong program management skills, including coordinating multiple workstreams, managing dependencies, and driving issues to closure.\n\n - Experience coordinating maintenance or incident response in production environments.\n\n - The ability to build practical processes where ownership and systems are still developing.\n\n - Clear communication with technical teams, external partners, and leadership, especially when a deployment or service commitment is at risk.\n\n - Willingness to travel for site assessments, deployments, partner reviews, and operational needs.\n\n\nHELPFUL EXPERIENCE\n\n - GPU infrastructure, high-density racks, liquid cooling, or high-speed network fabrics.\n\n - Managing colocation providers, installation contractors, or remote-hands services across multiple sites.\n\n - Earlier hands-on experience installing, troubleshooting, or maintaining data center equipment.\n\n - Defining vendor scopes of work, acceptance criteria, and service-level expectations.\n\n - Hiring, mentoring, or leading data center technicians or infrastructure operations teams.\n\n - Building an internal operations capability alongside external service providers.\n\n\nWHAT SUCCESS LOOKS LIKE\n\nGPU capacity enters production on a dependable schedule, with installation quality and operational readiness verified. Partners understand their responsibilities and meet their commitments. Hardware issues are resolved promptly, records remain accurate, and each deployment strengthens the processes and team supporting the next."},{"id":"ccf76786-daf2-4704-b356-9dc003ee9775","title":"Supply Chain & Procurement Lead, GPU Infrastructure","department":"Operations","team":"Operations","employmentType":"FullTime","location":"San Mateo","secondaryLocations":[],"publishedAt":"2026-09-11T22:25:54.257+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressLocality":"San Mateo"}},"jobUrl":"https://jobs.ashbyhq.com/parasail/ccf76786-daf2-4704-b356-9dc003ee9775","applyUrl":"https://jobs.ashbyhq.com/parasail/ccf76786-daf2-4704-b356-9dc003ee9775/application","descriptionHtml":"Parasail builds inference infrastructure for open-weight AI models. Behind one API, we bring together distributed GPU capacity and optimize how workloads run across it, helping customers access reliable, high-performance inference without managing the underlying infrastructure.
We’re building the team that sources, deploys, and operates the hardware behind that platform.
You’ll own the supplier relationships, purchasing, and supply planning that support Parasail’s GPU infrastructure—from identifying hardware needs and securing supply through delivery, replacements, and lifecycle management.
This is an early role with responsibility for both strategy and execution. You’ll negotiate with vendors, manage purchasing and delivery commitments, resolve shortages, and build visibility into equipment availability and cost. Working closely with engineering, finance, and our Data Center Deployment & Operations TPM, you’ll ensure purchasing decisions support real deployment needs.
Initially, you’ll work directly with suppliers, hardware manufacturers, distributors, and logistics partners to get things done. As Parasail grows, you’ll build the processes, systems, and team needed to support a larger infrastructure footprint.
Own supplier relationships. Evaluate and manage hardware manufacturers, distributors, integrators, and logistics partners. Establish clear expectations for pricing, availability, quality, delivery, and support.
Lead sourcing and purchasing. Run vendor evaluations and requests for quotes, negotiate commercial terms, and manage purchases for GPUs, servers, networking equipment, racks, spares, and supporting hardware.
Build the supply plan. Translate capacity requirements and deployment schedules into purchasing plans. Account for equipment already available, supplier lead times, configuration changes, and budget constraints.
Drive delivery commitments. Track orders from placement through manufacturing, shipment, and receipt. Resolve delays and discrepancies, and develop alternatives when supply puts a deployment at risk.
Partner on technical decisions. Work with engineering to confirm specifications, evaluate substitutions, and understand how hardware choices affect cost, availability, compatibility, and deployment timing.
Manage inventory and lifecycle. Maintain visibility into equipment that is ordered, in transit, in storage, deployed, or awaiting repair. Coordinate spares, warranties, returns, replacements, and end-of-life disposition with operations.
Balance cost and availability. Evaluate total acquisition costs, payment terms, inventory exposure, and the cost of delayed capacity. Present clear purchasing recommendations to finance and leadership.
Build procurement systems and controls. Establish practical workflows for purchase approvals, order tracking, inventory reconciliation, and invoice discrepancies. Automate repetitive work and keep records reliable as volume grows.
Keep supply and deployment aligned. Provide dependable equipment milestones to the deployment TPM. Surface shortages, supplier risks, and decisions early, with recovery options and accountable owners.
Build the function. Define supplier performance measures, improve purchasing and planning practices, and help hire and develop the team as demand grows.
Experience owning hardware procurement, supply chain, or materials planning in cloud infrastructure, data centers, electronics, or another complex hardware environment.
Direct responsibility for supplier relationships, commercial negotiations, purchasing decisions, and delivery commitments.
Experience managing supply against changing demand, including shortages, long lead times, configuration changes, and excess inventory.
An understanding of server and infrastructure hardware sufficient to work effectively with engineering on specifications and purchasing tradeoffs.
Strong analytical skills and experience building planning tools, procurement workflows, or inventory reporting.
A track record of resolving problems across suppliers, logistics providers, finance, and technical teams.
Comfort handling detailed execution while building a more scalable operating process.
Clear communication about costs, commitments, risks, and decisions.
Sourcing GPUs, servers, networking equipment, or integrated rack systems.
Working with OEMs, ODMs, contract manufacturers, and hardware distributors.
Supporting GPU infrastructure deployments or introducing new hardware platforms.
Coordinating international shipments with logistics and trade-compliance specialists.
Managing hardware warranties, returns, spare-parts planning, and lifecycle transitions.
Building or leading a procurement, supply planning, or hardware operations team.
Parasail has dependable access to the equipment needed for its deployment plans. Supplier commitments and inventory records are trustworthy, purchasing decisions balance cost and availability, and supply risks are addressed before they delay capacity. The systems and team you build allow the function to scale with the business.
","descriptionPlain":"SUPPLY CHAIN & PROCUREMENT LEAD, GPU INFRASTRUCTURE\n\n\n\n\nABOUT PARASAIL\n\nParasail builds inference infrastructure for open-weight AI models. Behind one API, we bring together distributed GPU capacity and optimize how workloads run across it, helping customers access reliable, high-performance inference without managing the underlying infrastructure.\n\nWe’re building the team that sources, deploys, and operates the hardware behind that platform.\n\n\nABOUT THE ROLE\n\nYou’ll own the supplier relationships, purchasing, and supply planning that support Parasail’s GPU infrastructure—from identifying hardware needs and securing supply through delivery, replacements, and lifecycle management.\n\nThis is an early role with responsibility for both strategy and execution. You’ll negotiate with vendors, manage purchasing and delivery commitments, resolve shortages, and build visibility into equipment availability and cost. Working closely with engineering, finance, and our Data Center Deployment & Operations TPM, you’ll ensure purchasing decisions support real deployment needs.\n\nInitially, you’ll work directly with suppliers, hardware manufacturers, distributors, and logistics partners to get things done. As Parasail grows, you’ll build the processes, systems, and team needed to support a larger infrastructure footprint.\n\n\nWHAT YOU’LL DO\n\n - Own supplier relationships. Evaluate and manage hardware manufacturers, distributors, integrators, and logistics partners. Establish clear expectations for pricing, availability, quality, delivery, and support.\n\n - Lead sourcing and purchasing. Run vendor evaluations and requests for quotes, negotiate commercial terms, and manage purchases for GPUs, servers, networking equipment, racks, spares, and supporting hardware.\n\n - Build the supply plan. Translate capacity requirements and deployment schedules into purchasing plans. Account for equipment already available, supplier lead times, configuration changes, and budget constraints.\n\n - Drive delivery commitments. Track orders from placement through manufacturing, shipment, and receipt. Resolve delays and discrepancies, and develop alternatives when supply puts a deployment at risk.\n\n - Partner on technical decisions. Work with engineering to confirm specifications, evaluate substitutions, and understand how hardware choices affect cost, availability, compatibility, and deployment timing.\n\n - Manage inventory and lifecycle. Maintain visibility into equipment that is ordered, in transit, in storage, deployed, or awaiting repair. Coordinate spares, warranties, returns, replacements, and end-of-life disposition with operations.\n\n - Balance cost and availability. Evaluate total acquisition costs, payment terms, inventory exposure, and the cost of delayed capacity. Present clear purchasing recommendations to finance and leadership.\n\n - Build procurement systems and controls. Establish practical workflows for purchase approvals, order tracking, inventory reconciliation, and invoice discrepancies. Automate repetitive work and keep records reliable as volume grows.\n\n - Keep supply and deployment aligned. Provide dependable equipment milestones to the deployment TPM. Surface shortages, supplier risks, and decisions early, with recovery options and accountable owners.\n\n - Build the function. Define supplier performance measures, improve purchasing and planning practices, and help hire and develop the team as demand grows.\n\n\nWHAT YOU BRING\n\n - Experience owning hardware procurement, supply chain, or materials planning in cloud infrastructure, data centers, electronics, or another complex hardware environment.\n\n - Direct responsibility for supplier relationships, commercial negotiations, purchasing decisions, and delivery commitments.\n\n - Experience managing supply against changing demand, including shortages, long lead times, configuration changes, and excess inventory.\n\n - An understanding of server and infrastructure hardware sufficient to work effectively with engineering on specifications and purchasing tradeoffs.\n\n - Strong analytical skills and experience building planning tools, procurement workflows, or inventory reporting.\n\n - A track record of resolving problems across suppliers, logistics providers, finance, and technical teams.\n\n - Comfort handling detailed execution while building a more scalable operating process.\n\n - Clear communication about costs, commitments, risks, and decisions.\n\n\nHELPFUL EXPERIENCE\n\n - Sourcing GPUs, servers, networking equipment, or integrated rack systems.\n\n - Working with OEMs, ODMs, contract manufacturers, and hardware distributors.\n\n - Supporting GPU infrastructure deployments or introducing new hardware platforms.\n\n - Coordinating international shipments with logistics and trade-compliance specialists.\n\n - Managing hardware warranties, returns, spare-parts planning, and lifecycle transitions.\n\n - Building or leading a procurement, supply planning, or hardware operations team.\n\n\nWHAT SUCCESS LOOKS LIKE\n\nParasail has dependable access to the equipment needed for its deployment plans. Supplier commitments and inventory records are trustworthy, purchasing decisions balance cost and availability, and supply risks are addressed before they delay capacity. The systems and team you build allow the function to scale with the business."}],"apiVersion":"1"}