Senior Software Engineer (Managed Kubernetes)

• Design, build, and maintain scalable control plane services, operators, and custom Kubernetes controllers; develop automation in Go/Python for end-to-end cluster lifecycle management — provisioning, upgrades, patching, and deletion
• Build GPU-aware orchestration systems, working within the platform architecture to support GPU scheduling and resource allocation
• Partner with the Network team on networking solutions for AI workloads: CNI integration (Cilium, Multus), high-performance fabrics (InfiniBand, RoCE), RDMA, and GPUDirect
• Write resilient systems that handle failure gracefully — timeouts, retries, backoff, and degraded-mode operation — across large-scale distributed environments
• Develop platform services for inference: model serving infrastructure, autoscaling based on inference load, and multi-model deployment patterns
• Build internal tools and CLIs that let ML/AI teams deploy and monitor their own inference services
• Support and debug production issues through on-call rotation

Benefits

• Health, dental vision
• 401k match
• 5 sick days and 12 paid holidays
• Flexible PTO
• Paid parental, medical, and caregiver leave

• Strong programming skills in Go and Python; ability to collaborate effectively on shared codebases
• Solid knowledge of Linux systems, networking, containers, and cloud infrastructure
• If you don’t meet all of these requirements but believe you may be a good fit, please still apply and provide a cover letter that helps us understand your experience and readiness for this role
• Experience with observability at scale: Prometheus, Grafana, distributed tracing, and building actionable alerting systems
• Take pride in owning and delivering core components of products and platforms
• Solid grasp of distributed systems fundamentals — fault tolerance, graceful degradation, and failure handling in large-scale environments
• Experience operating the control plane and low-level pieces of large-scale Kubernetes clusters
• Have 6+ years of experience in software engineering, with a track record of owning significant technical scope within a team (e.g., driving a project from design through production, or acting as a de facto tech lead on a workstream)
• Deep understanding of Kubernetes internals: controllers, schedulers, operators, CRDs, CSI, CNI, and the extension patterns that make Kubernetes powerful
• Past contributions to CNCF projects or Kubernetes SIGs a plus
• Exposure to storage architecture for AI/ML workloads
• Familiarity with GPU, InfiniBand, RDMA, or high-performance computing on Kubernetes
• Familiarity with HPC and traditional job schedulers (Slurm) and Kubernetes-native batch scheduling (KAI, Volcano, Kueue)
• Hands-on experience with NVIDIA’s GPU/networking ecosystem: GPU Operator, device plugins, DCGM, MIG, Network Operator, NCCL tuning, or similar
• Experience building and operating managed Kubernetes services (GKE, EKS, AKS, or similar) or working on Kubernetes control plane components