We're building the company which will de-risk the largest infrastructure build-out in history.
When people finance GPU clusters, the datacenters housing them, and the infrastructure powering them, they need "offtake" - meaning someone has signed a contract to lease the cluster for a period of time before its even built.
Financing a GPU cluster is inherently risky, since margins are thin and volumes are huge. Lenders don't want to take on the risk that cluster developers can't repay their loan, and cluster developers really don't want to risk not selling their cluster. As a result, risk is offloaded to the customer using fixed-price long-term contracts.
If you don't mitigate this customer risk, there's a bubble. This isn't SaaS anymore - application layer companies sign multi-year contracts for computer and inference, but sell to customers on monthly subscriptions. If you mess up a purchase, it's game over: a minor shift in your revenue growth rate might mean the difference between profit or bankruptcy. But what if companies could exit their contract by selling it back to the market?
Otherwise, as AI scales, compute only becomes available to folks who can effectively take on that risk. A 2-person startup in a San Francisco Victorian can't realistically sign a 5-year take or pay contract on $100m supercomputers. But they may be able to buy the month of liquidity that someone else sold back.
So that's what we make: a liquid market for GPU offtake.
We are a small team focused on making SFCompute engineering faster, more observable, and more reliable. Our work spans data infrastructure, developer experience, pre-production environments, and AI tooling — but the common thread isn't any specific domain. It's that we find the problems nobody else owns and make them solved problems.
Everyone on this team wears many hats. You'll work across the stack, collaborate with all parts of engineering, and regularly take on problems that don't fit neatly into a job description. If you want a narrow scope and a clear ticket queue, this team isn't it. If you want to have a large, legible impact on a small team building serious infrastructure, read on.
We're looking for a platform engineer who cares about the full pre-production experience — not just staging clusters, but the entire ecosystem of tooling that makes development fast and safe. Right now the gap between dev and prod is a real frustration. You'll close it. That means building a realistic staging environment, but it also means owning internal developer tooling, improving deployment pipelines, and eventually getting us off managed platforms like Vercel where self-hosting makes sense.
Design and build a pre-production EKS cluster that mirrors production fidelity without production cost
Own the infrastructure-as-code for the cluster (Terraform, Helm, or equivalent)
Integrate the cluster into CI/CD pipelines so changes are validated before they reach prod
Define promotion gates what has to pass in pre-prod before a change is eligible for production
Collaborate with platform and application engineers to understand what needs to be testable
Own and evolve internal developer tooling that improves how the team builds, tests, and ships
Drive migration off managed platforms (like Vercel) where self-hosting is the right call
Explore and implement A/B testing and feature flagging infrastructure to support safer, incremental rollouts
Monitor and maintain the pre-production environment over time
Hands-on EKS / Kubernetes experience you've provisioned and operated clusters, not just deployed workloads onto them
Experience with infrastructure-as-code tools (Terraform, CDK, or similar)
Familiarity with CI/CD systems (GitHub Actions, ArgoCD, or similar)
Strong operational instincts you know what "production-like" means and how to approximate it affordably
You can scope your own work. The pre-prod environment doesn't exist yet, so the first job is figuring out what it actually needs to be
Nice to have: experience with GPU workloads, bare metal networking, or marketplace-style platforms
We're shipping real workloads to bare-metal GPU clusters, and right now we validate too many infrastructure changes in production. That's the problem this role exists to solve. The cluster you build will be the default environment for every infrastructure change the team makes going forward. You'll own the design, the tooling, and the standards, with full backing from engineering leadership to do it right.
Team members are offered a competitive salary along with equity in the company
Yes, we sponsor visas and work permits
We match 401(k) plans up to 4%
We offer competitive medical, dental, vision insurance for employees and dependents and cover 100% of premiums
We offer unlimited paid time off as well as 10+ observed holidays
We offer biological, adoptive, and foster parents paid time off to spend quality time with family
We cover lunch daily for employees
You can buy as many books for the office as you want
The San Francisco Compute Company is committed to maintaining a workplace free from discrimination and harassment.
We make employment decisions based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, belief, national origin, social or ethical origin, age, physical, mental, or sensory disability, sexual orientation, gender identity or expression, marital status, civil union or domestic partnership status, past or present military service, HIV status, family medical history or genetic information, family or parental status including pregnancy, or any other status protected by law.
We welcome the opportunity to consider qualified applicants with prior arrest or conviction records. Our commitment to diversity includes hiring talented individuals regardless of their criminal history, in accordance with local, state, and federal laws, including San Francisco’s Fair Chance Ordinance and California’s ban-the-box laws.
If you require reasonable accommodation for any reason, please reach out to us at hiring@sfcompute.com
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A large, low-cost H100 cluster you can rent by the hour
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