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.
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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