Research Engineer, ML Infrastructure

The role is about making large model training fast, stable and cost efficient. That means working on the training code from whole models down to individual GPU kernels, and keeping long runs on big GPU clusters healthy. A strong candidate has deep hands-on experience with distributed training, performance profiling, and reliability work such as checkpointing and recovery, plus solid Python and PyTorch or JAX skills.

AI is the product here, so the work is about building the machinery that lets new model ideas be tested and scaled quickly. Tools that help write and tune code can speed up routine tasks, but deciding where a training run is losing time, and how to make it dependable, still takes careful human judgment. Working closely with researchers to turn new architectures into runs that scale stays a people-centered job.