Site Reliability Engineer, Compute

Fluidstack Production Engineering Team

Examples of key exciting problems the team is working on:

• Build the repair pipeline that keeps pace with a 10 GW fleet: 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.