Sr Kubernetes Engineer

Hybrid - 3 days/week in-office

USC and GC Preferred

Relocation Assistance Available!

In this role, you will design, implement, and optimize GPU-accelerated container platforms at scale, enabling high-performance workloads (AI/ML, HPC, LLM training) across hybrid or on-prem environments.

You will have deep expertise with both NVIDIA and Kubernetes ecosystems, including GPU scheduling, device plugins and custom operators.

Key responsibilities of the role include:

• Architecting and operating Kubernetes clusters optimized for GPU workloads, leveraging NVIDIA GPU Operator, Network Operator and DCGM
• Developing, deploying and maintaining custom Kubernetes operators and controllers to automate infrastructure services
• Integrating NVIDIA device plugins, Multi-Instance GPU (MIG) and GPU sharing features into the scheduling layer
• Optimizing GPU utilization and job placement through scheduler extensions, such as kube-scheduler plugins, Slurm and Volcano
• Collaborating with HPC, ML and DevOps teams to ensure multi-tenant, high-throughput cluster performance
• Driving observability and telemetry integrations using Prometheus, Grafana, DCGM Exporter and OpenTelemetry
• Implementing secure multi-user and multi-namespace GPU isolation, with RBAC and policy enforcement, such as OPA or Gatekeeper
• Maintaining CI/CD pipelines for Kubernetes infrastructure using GitOps, ArgoCD and FluxCD
• Contributing to infrastructure-as-code, using Terraform, Helm, and Kustomize
• Participating in performance tuning, incident response and production readiness reviews

Requirements

• Extensive experience with Kubernetes in production-grade environments and working with NVIDIA and Kubernetes, including GPU Operator, device plugin, NVML, MIG and DCGM
• Proficiency in Go or Python for operator development and Kubernetes controller logic
• Deep understanding of Kubernetes internals, including CRDs, RBAC, custom controllers and scheduler extensions
• Experience with GPU-intensive workloads, for example for LLMs, training pipelines and scientific computing
• Hands-on experience with Helm, Kustomize and GitOps workflows
• Familiarity with CNI plugins, especially NVIDIA CNI and Multus
• Experience with monitoring GPU metrics and cluster health using Prometheus and DCGM Exporter