ML Engineer, Infrastructure

We spend tens of millions per year on GPU compute to train tabular foundation models. The person who owns this infrastructure makes decisions worth millions of dollars: cluster architecture, scheduling efficiency, provider strategy, hardware selection. You own the full stack, from cluster operations and cost optimization to distributed training performance and the tooling layer that keeps researchers moving fast. You work directly with the research team and understand what they're doing well enough to make infrastructure decisions that actually help them.

Responsibilities

Own and evolve multi-cluster GPU infrastructure; Drive GPU utilization and training throughput; Architect the next generation of our infrastructure; Build the developer productivity layer; Own the compute budget

Requirements

3+ years building and operating production GPU infrastructure or distributed training systems at scale; Deep hands-on experience with Slurm and cluster management; Expert-level systems thinking: memory bandwidth, GPU profiling; Strong Python and genuine fluency with PyTorch internals; Track record of making infrastructure decisions that measurably improved training throughput or cost efficiency; Strong AI tooling skills. You use Claude Code, Cursor, or similar fluently to move fast without sacrificing quality

Nice to have

Experience operating at tens-of-millions-scale GPU spend; Multi-cloud or hybrid HPC/cloud infrastructure experience; Triton, CUDA, or custom kernel experience; Experience scaling from single cluster to multi-cluster orchestration; Background building experiment tracking, model registry, or ML pipeline tooling