Member of Technical Staff

Responsibilities

• Build and own a self-service compute platform for training and inference workloads.
• Provision, manage, and operate GPU fleets across multiple cloud providers.
• Develop scheduling and placement logic to efficiently allocate scarce GPU capacity.
• Support both long-running distributed training jobs and highly available production inference services.
• Write Kubernetes operators and CRDs and manage multi-cluster GPU orchestration.
• Build fault tolerance, autoscaling, and observability to handle node loss, provider failures, and capacity changes.
• Set technical direction and develop platform architecture and roadmap with inference and cloud infrastructure teams.

Requirements

• Deep Kubernetes experience, including custom operators, CRDs, and multi-cluster federation.
• Experience managing GPU clusters at scale with NVIDIA hardware, CUDA, and InfiniBand or RoCE networking.
• Experience orchestrating compute across multiple clouds such as CoreWeave, AWS, or GCP.
• Strong distributed systems fundamentals in scheduling, resource allocation, and fault tolerance.
• Ability to write infrastructure and systems-level code in Go, Rust, or C++.
• Experience supporting both long-running training jobs and high-availability inference services.
• Additional valued experience includes vLLM, SGLang, TensorRT-LLM, Slurm, GPU kernel development with CUDA or Triton, InfiniBand, RoCE, RDMA, Prometheus, Grafana, or Weights & Biases.