5-7 years of experience in production engineering or related field.
Strong understanding of hardware failure modes, especially at firmware and silicon levels.
Demonstrated capacity for quick learning and adaptability in unfamiliar domains.
Proficiency in deploying production automation that supports other teams.
Experience with AI tooling and frameworks, including LLM APIs and agentic frameworks.
Responsibilities
Own health metrics and alerting for the entire compute fleet.
Automate the deployment and repair process for compute failures.
Design and manage the GPU qualification platform to define production readiness.
Manage firmware-level telemetry and logging at fleet scale.
Ensure end-to-end reliability and scalability of the compute fleet.
Benefits
Competitive total compensation package including salary and equity.
Retirement or pension plan aligned with local norms.
Health, dental, and vision insurance provided.
Generous Paid Time Off (PTO) policy based on local standards.
Full Job Description
How We Operate
Extreme 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 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
The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.
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. This range represents our good faith estimate of the compensation for this role at the time of posting. Total compensation may also include equity in the form of stock options.
