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AI/ML On-site

Infrastructure SRE - HPC

Job Overview

We are hiring a team of specialists rather than a set of identical generalists. We expect candidates to bring genuine depth in one and working fluency across the others, because on a shared fleet a storage problem often first appears as a training hang, and the engineer on call must route an incident correctly before anyone can resolve…

Key points

  1. Operate the GPU fleet end to end across training and serving - provisioning, observability, capacity, and fleet health.

  2. Hold a meaningful on-call rotation, write runbooks that hold up under pressure, and drive postmortems that produce durable fixes.

  3. Build the internal tooling the team relies on, rather than operating off-the-shelf systems alone.

  4. Partner with ML and platform teams to keep large runs alive and serving latency predictable.

  5. Demonstrated on-call ownership of infrastructure that mattered, with a track record of postmortems that led to real change.

  6. Proficiency in Python or Go, used to build and maintain internal tooling.

  7. Working fluency across all five areas of focus below - enough to recognize, triage, and route a problem outside your specialty…

  8. For the Storage and Fabric areas of focus, we will weigh deep domain expertise against the GPU-cluster requirement…

Skills

KubernetesPythonNode.jsREST APIsMachine Learning

Experience: 5+ years · Job type: Full-time

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