Compute Deployment Engineer

Compute Deployment Engineer LocationNew York, NY; Austin, TX; San Francisco, CA; Seattle, WA; U.S. RemoteEmployment TypeFull timeLocation TypeOn-siteDepartmentDeliveryCompensationEstimated Base Salary $197K – $227K • Offers EquityTo provide greater transparency to candidates, we share base pay ranges for all US-based job postings. Our compensation package includes base salary, equity for all full time roles, benefits, and, for applicable roles, commissions plans.This range reflects a good-faith estimate based on the applicable pay scale, the range previously established for this role, the compensation of individuals currently in equivalent positions, and/or the budgeted amount for the position. Actual compensation may vary based on experience, qualifications, and other factors.We welcome compensation discussions if this range doesn't meet your requirements. Outstanding candidates may be eligible for adjusted terms, plus meaningful equity that ensures you benefit directly from the company's long-term performance.Total compensation may also include equity in the form of restricted stock units. About FluidstackWe exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI.We hire people who care deeply about this problem space. If that is you, please apply!How We OperateExtreme ownership. Full autonomy. Own things end to end often taking on scope outside your core role without being asked to get things done.Velocity. We drive everything forward as fast as possible.First principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.The Infrastructure TeamExamples of key problems the team is working onBring gigawatts of accelerators from first power-on to production. Facility availability to ready-for-service across thousands of racks per site, with a new data hall landing every few weeks.Make rack qualification faster than the fleet grows. Firmware baselines, burn-in, and cluster validation proven on every rack before a customer workload touches it, at a pace that never becomes the critical path.Scale by tooling, not headcount. Deployed megawatts grow severalfold next year while the team stays near-flat, because anything done twice by hand becomes software.Role ScopeOwn compute turn-up from facility availability to ready-for-service: the stretch after the network hands off and before customers run workloads.Qualify racks at scale: establish firmware baselines, configure BMC and BIOS, run burn-in, and validate at node and cluster level across hundreds of racks per site on GPU and custom accelerator platforms.Drive qualification through the base-management Kubernetes platform and provisioning stack (discovery, imaging, firmware updates, shared services), burning down qual queues with tooling rather than manual runs.Triage hardware failures found in qualification: isolate to component, drive RMA and vendor escalation, and feed failure patterns back into the qual gates.Run turn-up remotely by default, with on-site pulses of roughly a week per data hall as new halls reach...