Member of Technical Staff

Responsibilities

• Build a self-serve compute platform for launching training jobs and operating inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure.
• Own GPU fleet provisioning, lifecycle management, reliability, and capacity integration across cloud providers.
• Build scheduling and placement logic to find available capacity, efficiently pack workloads, and match workloads to appropriate hardware.
• Support long-running distributed training jobs and highly available, low-latency production inference services on the same fleet.
• Write Kubernetes operators and CRDs and manage multi-provider clusters.
• Build fault tolerance, autoscaling, and observability for resilience against node loss, provider failures, and capacity shifts.
• Set technical direction and partner with inference and cloud infrastructure engineers on platform architecture and roadmap.

Requirements

• Deep Kubernetes experience, including custom operators, CRDs, and multi-cluster federation.
• Experience managing GPU clusters at scale with NVIDIA hardware, CUDA, and high-performance networking such as InfiniBand or RoCE.
• Experience orchestrating compute across multiple clouds, such as CoreWeave, AWS, or GCP.
• Strong distributed-systems fundamentals, including scheduling, resource allocation, and fault tolerance under load.
• Infrastructure and systems-level programming experience in Go, Rust, or C++.
• Experience supporting long-running training jobs and high-availability inference services.
• End-to-end ownership and comfort working independently in ambiguous environments.

Nice to Have

• Experience with inference-serving stacks such as vLLM, SGLang, or TensorRT-LLM.
• Experience with Slurm or other HPC schedulers.
• GPU kernel experience with CUDA or Triton.
• Production experience with InfiniBand, RoCE, or RDMA.
• Observability experience for ML workloads with Prometheus, Grafana, or Weights & Biases.