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.
Sobre a vaga
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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