Sail builds the world's most efficient software for inference (processing LLM tokens) and agent hosting (cloud VMs). Together, our technologies allow our customers to deploy AI agents at large scale to do the most challenging work.
In this role, you'll focus on Sailboxes, our cost-efficient CPU sandbox for long-horizon agents. You’ll be one of the first engineers on a rapidly growing product-line and write code at every part of the agent runtime, from low-level performance work on our custom networking stack to building large scale systems that maximize our efficiency.
What you’ll do
Dig into the guts of our custom FirecrackerVM fork to extract every ounce of performance possible from our platform.
Model historic usage patterns and implement scheduling algorithms that maximize our fleet utilization and autoscale intelligently while minimizing latency.
Design and implement highly-available distributed systems to handle millions of operations per second.
Build deep monitoring tooling and features that make Sailboxes the most delightful experience for both humans and agents alike.
Own future product directions for Sailboxes (Windows/Mac sandboxes, high-efficiency storage, etc.).
What we’re looking for
Strong distributed systems fundamentals (concurrency, networking, databases, performance engineering). Ideally experience building and maintaining software for large-scale production distributed systems.
Experience with cloud infrastructure (GCP, AWS, Azure), container orchestration, and/or multi-cloud networking.
You take a product-focused approach to platform work and care deeply about building solutions that are robust, scalable, and ergonomic for users.
Bonus: experience with FirecrackerVMs.
Meet the CTO, who will ask about your experience and share as much detail about Sail as you want to hear.
A technical interview with one of our Sailbox engineers. This will also be an opportunity to ask any questions about the Sailbox product and team.
Come in to Sail's SF office for an interview day. Meet the whole team, then you'll have 3-4 hours to design and implement a solution to a problem that closely simulates the work we do daily. AI assistance is highly encouraged, and we'll provide a laptop with all the best tools set up. Finish with a short presentation describing your process, learnings, and results.
Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)
We work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.
","descriptionPlain":"Sail builds the world's most efficient software for inference (processing LLM tokens) and agent hosting (cloud VMs). Together, our technologies allow our customers to deploy AI agents at large scale to do the most challenging work.\n\nIn this role, you'll focus on Sailboxes, our cost-efficient CPU sandbox for long-horizon agents. You’ll be one of the first engineers on a rapidly growing product-line and write code at every part of the agent runtime, from low-level performance work on our custom networking stack https://www.sailresearch.com/blog/performing-live-migrations-of-massive-vms-at-scale to building large scale systems that maximize our efficiency.\n\n\n\nWhat you’ll do\n\n - Dig into the guts of our custom FirecrackerVM fork to extract every ounce of performance possible from our platform.\n\n - Model historic usage patterns and implement scheduling algorithms that maximize our fleet utilization and autoscale intelligently while minimizing latency.\n\n - Design and implement highly-available distributed systems to handle millions of operations per second.\n\n - Build deep monitoring tooling and features that make Sailboxes the most delightful experience for both humans and agents alike.\n\n - Own future product directions for Sailboxes (Windows/Mac sandboxes, high-efficiency storage, etc.).\n\nWhat we’re looking for\n\n - Strong distributed systems fundamentals (concurrency, networking, databases, performance engineering). Ideally experience building and maintaining software for large-scale production distributed systems.\n\n - Experience with cloud infrastructure (GCP, AWS, Azure), container orchestration, and/or multi-cloud networking.\n\n - You take a product-focused approach to platform work and care deeply about building solutions that are robust, scalable, and ergonomic for users.\n\n - Bonus: experience with FirecrackerVMs.\n\n\nINTERVIEW PROCESS\n\n 1. Meet the CTO, who will ask about your experience and share as much detail about Sail as you want to hear.\n\n 2. A technical interview with one of our Sailbox engineers. This will also be an opportunity to ask any questions about the Sailbox product and team.\n\n 3. Come in to Sail's SF office for an interview day. Meet the whole team, then you'll have 3-4 hours to design and implement a solution to a problem that closely simulates the work we do daily. AI assistance is highly encouraged, and we'll provide a laptop with all the best tools set up. Finish with a short presentation describing your process, learnings, and results.\n\n 4. Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)\n\n\n\n\nLIFE AT SAIL\n\nWe work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.","compensation":{"compensationTierSummary":null,"scrapeableCompensationSalarySummary":null,"compensationTiers":[],"summaryComponents":[]}},{"id":"c4389764-557e-4a14-875e-b318b448ddc5","title":"Member of Technical Staff - Distributed Systems","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"San Francisco","shouldDisplayCompensationOnJobPostings":true,"secondaryLocations":[],"publishedAt":"2026-05-18T20:34:46.510+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/sail/c4389764-557e-4a14-875e-b318b448ddc5","applyUrl":"https://jobs.ashbyhq.com/sail/c4389764-557e-4a14-875e-b318b448ddc5/application","descriptionHtml":"Sail is the foundation of useful, agentic AI. We are here to take a big swing at the most ambitious engineering challenge of our careers. Everyone working at Sail will become an expert; nothing less will do in our immensely competitive market.
Build the systems that make AI inference fast, reliable, and cost-efficient at global scale. You’ll design the control plane that schedules and autoscales our enormous queue of tokens over a diverse fleet of machines, spread all over the world.
What you’ll do
Design and implement high-performance schedulers (admission control, queuing, priority, fairness, preemption, bin packing).
Build global routing and traffic management (latency-aware dispatch, predictive autoscaling, failover strategies, cache-aware routing).
LLM-specific routing optimizations, e.g. KV caching that lets us trade memory for compute, across the cascading layers of GPU RAM, CPU RAM, and NVMe flash.
Build deep observability: we want to trace every millisecond of our systems, and catch failures early enough that we can make things right before customers even notice.
What we’re looking for
Strong distributed systems fundamentals (concurrency, networking, databases, queues, performance engineering).
Eagerness to work with agents coupled with a healthy dose of skepticism. We start by focusing our attention on the highest-level design decisions first, and try to debate and justify each of decisions. Then, we use our finite review time on the critical pieces of the system that we must build good mental models of to do good work.
Bonus: experience with ML inference stacks (vLLM/SGLang), GPUs/accelerators, high-RPS systems (e.g. trading, messaging)
Meet the CTO, who will ask about your experience, and share as much technical detail about Sail as you want to hear. This is the first step because we respect your time.
Share an online whiteboard with a team member and work through a technical problem. We spend a lot of time at whiteboards, building intuition about complex systems together. It's a great way for us to see how you communicate technically, and a even better way for you to see what working at Sail is like.
Come in to Sail's SF office for an interview day. Meet the whole team, and work on a bunch of problems that closely simulates the work we do daily. We'll also ask you to give us a 20-30min 'chalk talk' about an interesting problem you've worked on before.
Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)
We work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.
","descriptionPlain":"Sail is the foundation of useful, agentic AI. We are here to take a big swing at the most ambitious engineering challenge of our careers. Everyone working at Sail will become an expert; nothing less will do in our immensely competitive market.\n\n\n\n\nBuild the systems that make AI inference fast, reliable, and cost-efficient at global scale. You’ll design the control plane that schedules and autoscales our enormous queue of tokens over a diverse fleet of machines, spread all over the world.\n\n\n\nWhat you’ll do\n\n - Design and implement high-performance schedulers (admission control, queuing, priority, fairness, preemption, bin packing).\n\n - Build global routing and traffic management (latency-aware dispatch, predictive autoscaling, failover strategies, cache-aware routing).\n\n - LLM-specific routing optimizations, e.g. KV caching that lets us trade memory for compute, across the cascading layers of GPU RAM, CPU RAM, and NVMe flash.\n\n - Build deep observability: we want to trace every millisecond of our systems, and catch failures early enough that we can make things right before customers even notice.\n\n\n\nWhat we’re looking for\n\n - Strong distributed systems fundamentals (concurrency, networking, databases, queues, performance engineering).\n\n - Eagerness to work with agents coupled with a healthy dose of skepticism. We start by focusing our attention on the highest-level design decisions first, and try to debate and justify each of decisions. Then, we use our finite review time on the critical pieces of the system that we must build good mental models of to do good work.\n\n - Bonus: experience with ML inference stacks (vLLM/SGLang), GPUs/accelerators, high-RPS systems (e.g. trading, messaging)\n\n\n\n\nINTERVIEW PROCESS\n\n 1. Meet the CTO, who will ask about your experience, and share as much technical detail about Sail as you want to hear. This is the first step because we respect your time.\n\n 2. Share an online whiteboard with a team member and work through a technical problem. We spend a lot of time at whiteboards, building intuition about complex systems together. It's a great way for us to see how you communicate technically, and a even better way for you to see what working at Sail is like.\n\n 3. Come in to Sail's SF office for an interview day. Meet the whole team, and work on a bunch of problems that closely simulates the work we do daily. We'll also ask you to give us a 20-30min 'chalk talk' about an interesting problem you've worked on before. \n\n 4. Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)\n\n\nLIFE AT SAIL\n\nWe work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.","compensation":{"compensationTierSummary":"$200K – $300K • Offers Equity","scrapeableCompensationSalarySummary":"$200K - $300K","compensationTiers":[{"id":"eace7508-dce6-40d5-be67-c67977fdb085","tierSummary":"$200K – $300K • Offers Equity","title":null,"additionalInformation":null,"components":[{"id":"363ecd32-0745-4aef-921c-5627b928a67c","summary":"$200K – $300K","compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"id":"879de0a0-853c-4be5-a61a-3ea55a718efc","summary":"Offers Equity","compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}],"summaryComponents":[{"compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}},{"id":"79c33e81-85dd-47ca-9946-0540cb7298db","title":"Member of Technical Staff - Inference","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"San Francisco","shouldDisplayCompensationOnJobPostings":true,"secondaryLocations":[],"publishedAt":"2026-05-18T20:35:58.337+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/sail/79c33e81-85dd-47ca-9946-0540cb7298db","applyUrl":"https://jobs.ashbyhq.com/sail/79c33e81-85dd-47ca-9946-0540cb7298db/application","descriptionHtml":"Sail builds the world's most efficient software for inference (processing LLM tokens) and agent hosting (cloud VMs). Together, our technologies allow our customers to deploy AI agents at large scale to do the most challenging work.
In this role, you'll own token processing down to the lowest layers of the stack. You'll do things like: develop a new request scheduling strategy, achieve better communication/computation overlap, investigate novel schemes for increasing cache hit rates, or identify a better way to benchmark inference performance.
What you’ll do
Modify and extend state-of-the-art inference engines like vLLM and SGLang, and work on our own internal engine.
Understand every microsecond of GPU time spent during a forward pass. You'll be able to explain every kernel launch on an nsys profile.
Design and implement exotic parallelism schemes to work with \"interesting\" hardware topologies.
Write and debug GPU kernels to excel in specific regimes, such as cascade attention
What we’re looking for
Strong understanding of core LLM mechanics, like KV cache, mixture-of-experts, prefill vs. decode phases.
Interest in MLSys research - great ideas like speculative decoding and sparse attention come from research, that we need to follow closely.
Familiarity with modern, tile-based GPU programming, e.g. Triton, CUTLASS, ThunderKittens, etc. Or an interest in learning these!
Great interpersonal and technical communication. Please don't use LLMs to write prose. We desk-reject slopful cover letters and resumes.
Interview process
Meet the CTO, who will ask about your experience, and share as much technical detail about Sail as you want to hear. This is the first step because we respect your time.
Share an online whiteboard with a team member and work through a technical problem. We spend a lot of time at whiteboards, building intuition about complex systems together. It's a great way for us to see how you communicate technically, and a even better way for you to see what working at Sail is like.
Come in to Sail's SF office for an interview day. Meet the whole team, and work on a bunch of problems that closely simulates the work we do daily. We'll also ask you to give us a 20-30min 'chalk talk' about an interesting problem you've worked on before.
Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)
We work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise!). Everyone gets a Studio Display (or two) at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.
","descriptionPlain":"Sail builds the world's most efficient software for inference (processing LLM tokens) and agent hosting (cloud VMs). Together, our technologies allow our customers to deploy AI agents at large scale to do the most challenging work.\n\n\n\n\nIn this role, you'll own token processing down to the lowest layers of the stack. You'll do things like: develop a new request scheduling strategy, achieve better communication/computation overlap, investigate novel schemes for increasing cache hit rates, or identify a better way to benchmark inference performance.\n\n\n\nWhat you’ll do\n\n - Modify and extend state-of-the-art inference engines like vLLM and SGLang, and work on our own internal engine.\n\n - Understand every microsecond of GPU time spent during a forward pass. You'll be able to explain every kernel launch on an nsys profile.\n\n - Design and implement exotic parallelism schemes to work with \"interesting\" hardware topologies.\n\n - Write and debug GPU kernels to excel in specific regimes, such as cascade attention https://flashinfer.ai/2024/02/02/cascade-inference.html\n\nWhat we’re looking for\n\n - Strong understanding of core LLM mechanics, like KV cache, mixture-of-experts, prefill vs. decode phases.\n\n - Interest in MLSys research - great ideas like speculative decoding and sparse attention come from research, that we need to follow closely.\n\n - Familiarity with modern, tile-based GPU programming, e.g. Triton, CUTLASS, ThunderKittens, etc. Or an interest in learning these!\n\n - Great interpersonal and technical communication. Please don't use LLMs to write prose. We desk-reject slopful cover letters and resumes.\n\n\nInterview process\n\n 1. Meet the CTO, who will ask about your experience, and share as much technical detail about Sail as you want to hear. This is the first step because we respect your time.\n\n 2. Share an online whiteboard with a team member and work through a technical problem. We spend a lot of time at whiteboards, building intuition about complex systems together. It's a great way for us to see how you communicate technically, and a even better way for you to see what working at Sail is like.\n\n 3. Come in to Sail's SF office for an interview day. Meet the whole team, and work on a bunch of problems that closely simulates the work we do daily. We'll also ask you to give us a 20-30min 'chalk talk' about an interesting problem you've worked on before.\n\n 4. Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)\n\n\n\n\nLIFE AT SAIL\n\nWe work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise!). Everyone gets a Studio Display (or two) at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.","compensation":{"compensationTierSummary":"$200K – $300K • Offers Equity","scrapeableCompensationSalarySummary":"$200K - $300K","compensationTiers":[{"id":"7e5a191a-4be7-4bd3-92d0-88916d3fcb19","tierSummary":"$200K – $300K • Offers Equity","title":null,"additionalInformation":null,"components":[{"id":"7fb0f4b8-89d5-40b0-aa54-7dedd808f2c2","summary":"$200K – $300K","compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"id":"3c3eb53c-2238-48f9-b7a3-5a954f59318b","summary":"Offers Equity","compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}],"summaryComponents":[{"compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}},{"id":"3e00a184-fe57-408c-9dad-20fc2d1ef84f","title":"Member of Technical Staff - Agent Engineering","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"San Francisco","shouldDisplayCompensationOnJobPostings":true,"secondaryLocations":[],"publishedAt":"2026-05-18T20:36:33.824+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/sail/3e00a184-fe57-408c-9dad-20fc2d1ef84f","applyUrl":"https://jobs.ashbyhq.com/sail/3e00a184-fe57-408c-9dad-20fc2d1ef84f/application","descriptionHtml":"Sail is the foundation of useful, agentic AI. We are here to take a big swing at the most ambitious engineering challenge of our careers. Everyone working at Sail will become an expert; nothing less will do in our immensely competitive market.
Inference is just one piece of an effective background agent. Let's design and build the rest of the system, that turns billions of tokens into the best possible answers.
What you’ll do
Design custom evals for multi-turn, massively parallel agents.
Build agent harnesses to improve open model (Deepseek, Qwen, Llama) performance. Claude Code is all about agent/harness codesign; let's do the same for open source!
Automate prompt optimization techniques like DSPy.
What we’re looking for
Experience building AI agents.
Familiarity with open source models.
Meet the CTO, who will ask about your experience, and share as much technical detail about Sail as you want to hear. This is the first step because we respect your time.
We'll give you a take-home assignment that challenges you to work with agents in a systematic way, and evaluate them clearly and objectively
Come in to Sail's SF office for an interview day. Meet the whole team, then present your work, including your process, learnings, and results.
Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)
We work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.
","descriptionPlain":"Sail is the foundation of useful, agentic AI. We are here to take a big swing at the most ambitious engineering challenge of our careers. Everyone working at Sail will become an expert; nothing less will do in our immensely competitive market.\n\nInference is just one piece of an effective background agent. Let's design and build the rest of the system, that turns billions of tokens into the best possible answers.\n\n\n\nWhat you’ll do\n\n - Design custom evals for multi-turn, massively parallel agents.\n\n - Build agent harnesses to improve open model (Deepseek, Qwen, Llama) performance. Claude Code is all about agent/harness codesign; let's do the same for open source!\n\n - Automate prompt optimization techniques like DSPy.\n\nWhat we’re looking for\n\n - Experience building AI agents.\n\n - Familiarity with open source models.\n\n\nINTERVIEW PROCESS\n\n 1. Meet the CTO, who will ask about your experience, and share as much technical detail about Sail as you want to hear. This is the first step because we respect your time.\n\n 2. We'll give you a take-home assignment that challenges you to work with agents in a systematic way, and evaluate them clearly and objectively\n\n 3. Come in to Sail's SF office for an interview day. Meet the whole team, then present your work, including your process, learnings, and results.\n\n 4. Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)\n\n\nLIFE AT SAIL\n\nWe work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.","compensation":{"compensationTierSummary":"$200K – $300K • Offers Equity","scrapeableCompensationSalarySummary":"$200K - $300K","compensationTiers":[{"id":"a5c6a725-1d91-4018-8098-161b642bd6a4","tierSummary":"$200K – $300K • Offers Equity","title":null,"additionalInformation":null,"components":[{"id":"7c2003b8-8541-4a2a-8fa6-2455d48ae3ae","summary":"$200K – $300K","compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"id":"986e9ff8-bb5f-4572-ad06-27a8161fd41f","summary":"Offers Equity","compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}],"summaryComponents":[{"compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}},{"id":"b5a339ba-5e80-4bad-a892-d814e88acfae","title":"Strategic Finance Lead","department":"Operations","team":"Operations","employmentType":"FullTime","location":"San Francisco","shouldDisplayCompensationOnJobPostings":true,"secondaryLocations":[],"publishedAt":"2026-06-25T08:45:46.380+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/sail/b5a339ba-5e80-4bad-a892-d814e88acfae","applyUrl":"https://jobs.ashbyhq.com/sail/b5a339ba-5e80-4bad-a892-d814e88acfae/application","descriptionHtml":"Sail is the foundation of useful, agentic AI. We are here to take a big swing at the most ambitious engineering challenge of our careers. Everyone working at Sail will become an expert; nothing less will do in our immensely competitive market.
We're hiring a Strategic Finance Lead as our first dedicated finance hire. This is a builder's role — you're not stepping into existing systems, you're creating them.
What you’ll do
Build and own Sail's operating P&L — the single source of truth connecting GPU cluster unit economics (cost per GPU-hour, utilization, depreciation/useful life, power costs) and how it flows down to product pricing, unit economics, and actual financial performance
Partner crossfunctionally on board materials: financial modeling, data room preparation, and diligence support
Stand up Sail's first real FP&A process: budgeting and forecasting, sized appropriately for an early-stage company rather than over-built for one
Develop the headcount and opex planning model as the team grows, and own burn rate and runway tracking
What we’re looking for
5-8+ years in finance roles with real modeling depth — investment banking, growth equity/private equity, corporate development, or strategic finance/FP&A at a high-growth company. Infrastructure or capital-intensive business experience (data centers, energy, equipment leasing, or similar) is a strong plus
Genuine fluency in building financial models from scratch — not just maintaining someone else's template. You should be comfortable building an operating P&L and pro forma model without a lot of hand-holding
Comfort with ambiguity and a preference for building the right-sized process rather than the most sophisticated one. We're early — judgment about what to build now vs. later matters as much as technical skill
Bonus: prior exposure to revenue recognition for usage-based/consumption billing, or experience inside a GPU cloud, data center, or other infrastructure-as-a-service business
Meet the Head of BD. You'll be working in close collaboration on compute procurement, GTM decisions, and the financial engineering behind them.
Meet the CEO, who will ask about your experience and share as much detail about Sail as you want to hear.
Take-home case study walking through a regular financial analysis we think through on a daily basis.
Come in to Sail's SF office for an interview day. Meet the whole team, then you'll have the opportunity to present your case study describing your process, learnings, and results.
Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)
We work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.
","descriptionPlain":"Sail is the foundation of useful, agentic AI. We are here to take a big swing at the most ambitious engineering challenge of our careers. Everyone working at Sail will become an expert; nothing less will do in our immensely competitive market.\n\nWe're hiring a Strategic Finance Lead as our first dedicated finance hire. This is a builder's role — you're not stepping into existing systems, you're creating them. \n\nWhat you’ll do\n\n - Build and own Sail's operating P&L — the single source of truth connecting GPU cluster unit economics (cost per GPU-hour, utilization, depreciation/useful life, power costs) and how it flows down to product pricing, unit economics, and actual financial performance\n\n - Partner crossfunctionally on board materials: financial modeling, data room preparation, and diligence support\n\n - Stand up Sail's first real FP&A process: budgeting and forecasting, sized appropriately for an early-stage company rather than over-built for one\n\n - Develop the headcount and opex planning model as the team grows, and own burn rate and runway tracking\n\nWhat we’re looking for\n\n - 5-8+ years in finance roles with real modeling depth — investment banking, growth equity/private equity, corporate development, or strategic finance/FP&A at a high-growth company. Infrastructure or capital-intensive business experience (data centers, energy, equipment leasing, or similar) is a strong plus\n\n - Genuine fluency in building financial models from scratch — not just maintaining someone else's template. You should be comfortable building an operating P&L and pro forma model without a lot of hand-holding\n\n - Comfort with ambiguity and a preference for building the right-sized process rather than the most sophisticated one. We're early — judgment about what to build now vs. later matters as much as technical skill\n\n - Bonus: prior exposure to revenue recognition for usage-based/consumption billing, or experience inside a GPU cloud, data center, or other infrastructure-as-a-service business\n\n\nINTERVIEW PROCESS\n\n 1. Meet the Head of BD. You'll be working in close collaboration on compute procurement, GTM decisions, and the financial engineering behind them. \n\n 2. Meet the CEO, who will ask about your experience and share as much detail about Sail as you want to hear.\n\n 3. Take-home case study walking through a regular financial analysis we think through on a daily basis.\n\n 4. Come in to Sail's SF office for an interview day. Meet the whole team, then you'll have the opportunity to present your case study describing your process, learnings, and results.\n\n 5. Offer. Once the team decides we want to work with you, we make a strong offer quickly and will be quite persistent over email/text/calls :)\n\n\nLIFE AT SAIL\n\nWe work out of a beautiful, sunny office in downtown San Francisco. All meals are on us (and actually great; SF is a food paradise and it would be a shame to eat only bowl slop). Everyone gets a Studio Display at their desk. We are serious about investing in anything that saves us time or energy. There are six different ways to make coffee or tea in the office. A friendly (hypoallergenic) black cat named Coco visits occasionally.\n\n","compensation":{"compensationTierSummary":"$200K – $300K • Offers Equity","scrapeableCompensationSalarySummary":"$200K - $300K","compensationTiers":[{"id":"8df20359-dcd0-485c-9c41-e309378b4cbe","tierSummary":"$200K – $300K • Offers Equity","title":null,"additionalInformation":null,"components":[{"id":"30668c66-4bde-4708-8ed5-19ed8e933f55","summary":"$200K – $300K","compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"id":"38803cc6-ca6d-4d04-a16a-beb61f4bacef","summary":"Offers Equity","compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}],"summaryComponents":[{"compensationType":"Salary","interval":"1 YEAR","currencyCode":"USD","minValue":200000,"maxValue":300000},{"compensationType":"EquityPercentage","interval":"NONE","currencyCode":null,"minValue":null,"maxValue":null}]}}],"apiVersion":"1"}