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FluidstackFluidstackSan Francsisco, CA

Site Reliability Engineer, Compute

Own end-to-end health, reliability, and automation for a massive GPU compute fleet at Fluidstack. Build metrics, repair pipelines, GPU qualification platforms, and low-level BMC/Redfish tooling while driving incidents and using AI coding tools daily. Hardware intuition and comfort with ambiguity required.

175k – 300k
On-site5+ YOEDevOps / SRE

About the role

Responsibilities

  • Own compute fleet health end to end: build metrics pipelines, alerting, and unified health view for every GPU in production across Kubernetes-orchestrated workloads and bare metal at scale.
  • Turn deployment and repair into a pipeline: build and own automation that takes a compute failure from detection through triage, parts management, and return to service.
  • Design and expand the GPU qualification platform for burn-in, performance baselining, and NPI execution for every new GPU generation; define what "production-ready" means.
  • Own Redfish and BMC tooling, including firmware-level telemetry, log collection at fleet scale, and the low-level access layer.
  • Own end-to-end reliability, scalability, and operation of the compute fleet at scale through aggressive automation, tooling, and incident discipline.
  • Build the repair pipeline that keeps pace with a 10 GW fleet, automating from fault detection through triage, RMA, and return to service.
  • Qualify every new GPU generation inside a 6-month build window.
  • Migrate live compute at construction speed across production sites.
  • See and own the entire fleet in real time at any scale: build observability and orchestration layer.
  • Debug, tune, and performance-test infrastructure that grows rapidly.

Requirements

  • Treat toil as a bug: manual steps in repair workflows are backlog items.
  • Instinct for hardware: comfortable reasoning about failure modes at firmware and silicon level.
  • Move toward ambiguity: build the map in unclear situations.
  • Learn at a steep slope: reach real competence in unfamiliar domains quickly.
  • Carry a pager: run incidents, write postmortems, fix systemic causes.
  • Fluent with AI tooling: use LLM APIs, MCP servers, agentic frameworks, Claude Code, Cursor, or similar daily.
  • 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.

Skills

KubernetesRedfishBmcPrometheusGrafanaGoPythonTemporalCadenceIpmi

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