ML Engineer (Training Infra), Foundational Models

About the Role

You will own the infrastructure that our next family of foundational models is trained on. This is a deep systems role: distributed training at scale, parallelism strategies, GPU kernel work, throughput and stability optimization, and the unglamorous reliability engineering that determines whether a frontier training run actually finishes.

The bar is high. A bad MFU number, a slow data loader, a switch flap — these are problems whose cost is measured in weeks and millions of dollars. You will be expected to find them before they happen, and to fix them fast when they do.

What You’ll Do

• Build, maintain, and continually push the limits of our distributed training stack across large GPU clusters.

• Design and implement parallelism strategies — data, tensor, pipeline, sequence, expert — and reason about which combinations make sense for which architectures and which scales.

• Profile and optimize end-to-end training throughput: kernel performance, communication overlap, memory layout, checkpointing, data loading.

• Write and tune custom GPU kernels (CUDA, Triton) where off-the-shelf is leaving performance on the table.

• Own the reliability of long-running training jobs — fault tolerance, checkpoint integrity, deterministic restarts, automated detection of slow nodes and silent corruption.

• Partner closely with researchers to