Member of Technical Staff

Responsibilities

• Build a self-serve compute platform that lets inference engineers and researchers launch training jobs and operate inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure.
• Operate the GPU fleet, including provisioning, lifecycle management, reliability, and capacity integration across providers.
• Solve for GPU scarcity by building scheduling and placement logic that finds available capacity across providers, packs it efficiently, and gets the right workload onto the right hardware.
• Support long-running distributed training jobs while guaranteeing the availability and latency of production inference services on the same fleet.
• Own Kubernetes for GPU orchestration, including writing operators and CRDs, and managing many clusters across providers.
• Build fault tolerance, autoscaling, and observability to keep the fleet utilized and let workloads survive node loss, provider hiccups, and capacity shifts without human intervention.
• Set technical direction across teams by partnering with inference and cloud infrastructure engineers to turn operational constraints into a coherent platform architecture and roadmap.

Qualifications

• Deep Kubernetes experience with custom operators, CRDs, and multi-cluster federation.
• Experience managing GPU clusters at scale, including NVIDIA hardware, CUDA, and high-performance networking (InfiniBand or RoCE).
• Experience orchestrating compute across multiple clouds (CoreWeave, AWS, GCP, or similar).
• Strong distributed systems fundamentals in scheduling, resource allocation, and fault tolerance under load.
• Proficiency writing infrastructure and systems-level code in Go, Rust, or C++.
• Experience supporting both long-running training jobs and high-availability inference services.

Nice-to-Haves

• Experience with inference serving stacks: vLLM, SGLang, or TensorRT-LLM.
• Experience with Slurm or other HPC schedulers.
• GPU kernel work in CUDA or Triton.
• Experience with high-speed interconnects: InfiniBand, RoCE, or RDMA in production.
• Observability for ML workloads: Prometheus, Grafana, or Weights & Biases.