Production Engineering Team
Examples of key exciting problems the team is working on:
- Build the repair pipeline that keeps pace with a fleet of 10s to 100s of GWs: at our scale, a GPU failure isn't a ticket. It's a throughput problem. We're building the automation that takes a chip from fault detection through triage, RMA, and return to service without human intervention.
- Qualify every new GPU generation inside a 6-month build window: our platform covers burn-in, performance baselining, and NPI execution. It has to define "production-ready" before a site goes live, not after. New hardware gets certified at speeds unheard of in the industry.
- Migrate live compute at construction speed: we're converting clusters across production sites simultaneously, bringing new sites online, and making Kubernetes-orchestrated bare metal sustainable at the pace we're building – multiple GW annually.
- See and own the entire fleet in real time, at any scale: build the observability and orchestration layer that makes hyperscale AI compute actually operable. Debug, tune, and performance-test infrastructure that grows by another site every few months.
Role Scope
- Own compute fleet health end to end. Build the metrics pipelines, alerting, and unified health view that tell you the true state of every GPU in production — across Kubernetes-orchestrated workloads and bare metal, at scale.
- Turn deployment/repair into a pipeline, not a procedure. Build and own the automation that takes a compute failure from detection through triage, parts management, and return to service. No one-off scripts, no heroics.
- Design and expand the GPU qualification platform. Burn-in, performance baselining, and NPI execution for every new GPU generation. You define what "good" looks like before hardware goes into production.
- Own Redfish and BMC tooling. Firmware-level telemetry, log collection at fleet scale, and the low-level access layer that repair automation and health tooling depend on.
- Own end-to-end reliability, scalability, and operation of the compute fleet at-scale. Fluidstack is building one of the largest GPU fleets in the world and that can only be accomplished with aggressive automation, tooling, and incident discipline.
What We're Looking For
- You treat toil as a bug. Manual steps in a repair workflow are a backlog item, not a job description.
- You have an instinct for hardware. You're comfortable reasoning about failure modes at the firmware and silicon level, not just the software stack above it.
- You move toward ambiguity, not away from it. You walk into the fog, build the map, and explain it to everyone else.
- You learn at a steep slope. You reach real competence in an unfamiliar domain fast. We value this over existing expertise.
- You carry a pager without flinching. You run the incident, write the postmortem, fix the systemic cause, and move on.
- You're fluent with AI tooling. LLM APIs, MCP servers, and agentic frameworks, and you drive Claude Code, Cursor, or similar every day.
- You've shipped production automation that other teams depend on, and you're comfortable in any language using AI coding tools.
Bonus:
- Hardware lifecycle management and RMA automation.
- BMC/Redfish or IPMI tooling.
- GPU qualification or burn-in frameworks.
- Workflow and orchestration engines (Temporal, Cadence).
- Metrics and alerting pipelines (Prometheus, Grafana).
- Go or Python.
Salary & Benefits
- Competitive total compensation package (salary + equity).
- Retirement or pension plan, in line with local norms.
- Health, dental, and vision insurance.
- Generous PTO policy, in line with local norms.
The base salary range for this position is $175,000 - $300,000 per year, depending on experience, skills, qualifications, and location.
FluidstackSan Francsisco, CA