Requirements

Deep expertise in ML/HPC infrastructure: Experience with GPU/TPU clusters, distributed training frameworks (JAX, PyTorch, TensorFlow), and high-performance computing (HPC) environments

Kubernetes at scale: Proven ability to deploy, manage, and troubleshoot cloud-native Kubernetes clusters for AI workloads

Strong programming skills: Proficiency in Python (for ML tooling) and Go (for systems engineering), with a preference for open-source contributions over reinventing solutions

Low-level systems knowledge: Familiarity with Linux internals, RDMA networking, and performance optimization for ML workloads

Research collaboration experience: A track record of working closely with AI researchers or ML engineers to solve infrastructure challenges

Self-directed problem‑solving: The ability to identify bottlenecks, propose solutions, and drive impact in a fast‑paced environment

If some of the above doesn’t line up perfect...