Kubernetes auditors judge cluster-operations scenarios, manifest correctness and failure-mode troubleshooting in AI training tasks. It suits production platform or SRE engineers with several years of Kubernetes experience who can debug live clusters and write clear feedback.
About the role
Evaluate the quality, correctness, and production-readiness of Kubernetes tasks used to train and evaluate a frontier AI lab's models. You'll assess cluster-operations scenarios, manifest correctness, and failure-mode troubleshooting — and provide clear, rubric-based written feedback.
Basic Qualifications
- 3+ years hands-on production Kubernetes experience (EKS/GKE/AKS or self-managed)
- Deep understanding of cluster internals: CNI/DNS/ingress, PV/PVC storage, RBAC, and failure modes (CrashLoopBackOff, OOMKilled, scheduling/eviction)
- Experience authoring and reviewing manifests / Helm charts and debugging live cluster incidents
- Proficiency in Go, Python, or TypeScript
Preferred Qualifications
- CKA / CKAD certification
- Service-mesh, autoscaling, and observability experience (Istio, HPA, Prometheus/Grafana)
- Prior SRE / platform-engineering or task-grading experience
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