Member of Technical Staff (Software Engineer, GPU Cluster Infrastructure)

Required

• We expect you to have real depth in most of these:
• Deep Kubernetes experience — custom operators, CRDs, and multi-cluster federation, not just running kubectl apply.
• You've managed GPU clusters at scale: NVIDIA hardware, CUDA, and the networking that makes them fast (InfiniBand or RoCE).
• You've orchestrated compute across multiple clouds (CoreWeave, AWS, GCP, or similar) and understand how different each one really is.
• Strong distributed systems fundamentals: scheduling, resource allocation, and fault tolerance under load.
• You write infrastructure and systems-level code in Go, Rust or C++.
• You've supported both long-running training jobs and high-availability inference services, and you know why they pull infrastructure in opposite directions.
• You own problems end-to-end and do well when the path forward isn't laid out for you.
• Additional experience we value
• Inference serving stacks: vLLM, SGLang, or TensorRT-LLM.
• Slurm or other HPC schedulers.
• GPU kernel work in CUDA or Triton — not required, but notable.