Software Engineer, GPU Infrastructure
Fluidstack · San Francisco, CA
About this role
## About Fluidstack We exist to make humanity more free. Powerful AI will be the biggest lever for human choice—but only if models are aligned with what humanity actually wants. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. Fluidstack is 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—spanning hardware and software. ## How We Operate - **Be a barrel:** Full autonomy. Own things end to end; take on scope without being asked. - **Insane urgency:** Drive everything forward as fast as possible. - **Reason from first principles:** Challenge assumptions; no egos—the best idea wins. - **Love of the game:** Long hours at high intensity to push the frontier. - **Build something that actually matters:** Spend your time on work that impacts the world. ## Production Engineering Team — Key Problems - **Repair pipeline at hyperscale:** Build automation that takes a GPU from fault detection through triage, RMA, and return to service **without human intervention**. - **GPU qualification in ~6 months:** Burn-in, performance baselining, and NPI execution to define “production-ready” **before** sites go live. - **Migrate live compute at construction speed:** Convert clusters across production sites and make **Kubernetes-orchestrated bare metal** sustainable at multiple GW annually. - **Real-time fleet observability & orchestration:** Build the observability/orchestration layer to make hyperscale AI compute operable; debug, tune, and performance-test as the fleet grows. ## Role Scope - **Own compute fleet health end to end:** Build metrics pipelines, alerting, and a unified health view across **Kubernetes-orchestrated workloads and bare metal**. - **Turn deployment/repair into a pipeline:** Automate compute failures from detection through triage, parts management, and return to service (no one-off scripts). - **Design/expand the GPU qualification platform:** Burn-in, performance baselining, and NPI execution; define what “good” means before production. - **Own Redfish & BMC tooling:** Firmware-level telemetry, fleet-scale log collection, and low-level access layers used by repair/health tooling. - **Reliability, scalability, and operations at scale:** Build and operate the compute fleet with aggressive automation, tooling, and incident discipline. ## What We’re Looking For - Treat **toil as a bug**; manual steps belong on a backlog. - Strong **hardware instincts**; comfortable reasoning about failure modes at firmware/silicon levels. - Move toward **ambiguity**; build the map and explain it to others. - Learn quickly; reach real competence in unfamiliar domains. - Comfortable with **on-call/pager**: run incidents, write postmortems, fix systemic causes. - Fluent with **AI tooling** (LLM APIs, MCP servers, agentic frameworks); use tools like Claude Code/Cursor daily. - Shipped production automation other teams depend on; comfortable using AI coding tools in any language. - **Bonus:** Hardware lifecycle management & RMA automation; BMC/Redfish/IPMI tooling; GPU qualification/burn-in frameworks; workflow/orchestration engines (Temporal/Cadence); metrics/alerting (Prometheus/Grafana); Go or Python. ## Salary & Benefits - Competitive total compensation (**salary + equity**) - Retirement/pension plan (local norms) - Health, dental, and vision i
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