Production Engineer, Compute

Responsibilities

• Own compute fleet health end to end. Build metrics pipelines, alerting, and unified health view that tell you the true state of every GPU and TPU in production — across Kubernetes orchestrated workloads and bare metal, at scale.
• Turn repair into a pipeline, not a procedure. Build and own automation to take a compute failure from detection through triage, parts management, and return to service. No one-off scripts, no heroics.
• Design and expand the XPU qualification platform. Burn-in, performance baselining, and NPI execution for every new GPU and TPU generation. 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 idstack is building one of the largest XPU fleets in the world, which can only be accomplished with aggressive automation, tooling, and incident discipline.

• 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. Walk into the fog, build the map, and explain it to everyone else.
• You learn at a steep slope. Your competence in an unfamiliar domain fast. We value this over existing expertise.
• You carry a pager without flinching. Run the incident, write the postmortem, fix the systemic cause, and move on.
• You're fluent with AI tooling. LLM APIs, MCP servers, agentic frameworks, and drive Claude Code, Cursor, or similar every day.
• You have shipped production automation that other teams depend on, comfortable in any language using AI coding tools.
• Bonus:
  Hardware lifecycle management and RMA automation. BMC/Redfish or IPMI tooling. GPU/TPU qualification or burn-in frameworks. Workflow and orchestration engines (Temporal, Cadence). Metrics and alerting pipelines (Prometheus, Grafana). Go or Python.