Software Engineer, Compute (GPU)
About The Position
Fluidstack is building civilization-scale infrastructure for AI, aiming to deliver 10 to 100s of GWs of compute faster than anyone else. This involves rethinking every layer of the stack, from acquiring power and designing data centers to operating them with teams spanning hardware and software. The company emphasizes speed and scale as key differentiators. They hire individuals who are deeply committed to this problem space and operate with extreme ownership, velocity, first principles thinking, and a passion for the work. The Production Engineering Team is focused on critical problems such as building a repair pipeline for a large GPU fleet, qualifying new GPU generations rapidly, migrating live compute at construction speed, and developing the observability and orchestration layer for hyperscale AI compute. This role specifically focuses on owning the compute fleet health end-to-end. The engineer will build metrics pipelines, alerting, and a unified health view for all GPUs in production, across Kubernetes and bare metal. Key responsibilities include transforming deployment/repair into an automated pipeline, designing and expanding the GPU qualification platform (burn-in, performance baselining, NPI execution), and owning Redfish and BMC tooling for firmware-level telemetry and low-level access. The role demands end-to-end ownership of reliability, scalability, and operation of the compute fleet at scale, requiring aggressive automation, tooling, and incident discipline.
Requirements
- Treat toil as a bug. Manual steps in a repair workflow are a backlog item, not a job description.
- Have an instinct for hardware. Comfortable reasoning about failure modes at the firmware and silicon level, not just the software stack above it.
- Move toward ambiguity, not away from it. Walk into the fog, build the map, and explain it to everyone else.
- Learn at a steep slope. Reach real competence in an unfamiliar domain fast.
- Carry a pager without flinching. Run the incident, write the postmortem, fix the systemic cause, and move on.
- Fluent with AI tooling. LLM APIs, MCP servers, and agentic frameworks, and drive Claude Code, Cursor, or similar every day.
- Shipped production automation that other teams depend on.
- Comfortable in any language using AI coding tools.
Nice To Haves
- 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.
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/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.
- Design and expand the GPU qualification platform.
- Own Redfish and BMC tooling.
- Own end-to-end reliability, scalability, and operation of the compute fleet at-scale.
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.
- Equity in the form of stock options.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
