Software Engineer, Compute (GPU)

About the company

We exist to make humanity more free. Technology has historically given people more time for the things they want to do, instead of things they have 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. 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 and thrive in an environment of extreme ownership, velocity, and first-principles thinking.

About the team

The Production Engineering Team is tackling key problems at the frontier of AI infrastructure:

• 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 defines "production-ready" before a site goes live, not after.
• Migrate live compute at construction speed: 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 operable. Debug, tune, and performance-test infrastructure that grows by another site every few months.

Responsibilities

• 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 and 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. Eliminate one-off scripts and heroics.
• Design and expand the GPU qualification platform. Define burn-in, performance baselining, and NPI execution for every new GPU generation. Set the standard for "production-ready" before hardware goes live.
• Own Redfish and BMC tooling. Manage 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, which requires aggressive automation, tooling, and incident discipline.

Requirements

• 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. 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 skills:

• 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.

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.

Total compensation may also include equity in the form of restricted stock units. We are committed to pay equity and transparency.

Pay

Compensation range: $208K - $269K