We're looking for a Senior Research Engineer to join our Research team, developing and improving the systems behind large-scale distributed training, data processing, and inference. Our goal as an organization is to solve customer problems and improve our products quickly through model development and measurement — and how fast we move depends on how quickly anyone here can run an experiment, measure it, and find out what's wrong. Raising that ceiling is the heart of this role. You'll be working inside the pipeline you're improving, not alongside it
The ideal candidate has a deep understanding of modern deep learning systems, combined with strong engineering expertise across JAX and TPUs, layer-level optimization, large-scale distributed training, streaming, low-latency and asynchronous inference, inference compilers, and advanced parallelization techniques
This is a cross-functional role. You'll work closely with our researchers, our infrastructure team, and production engineering — not as a handoff point, but as the person who learns enough of each domain to follow problems through to resolution. At times you'll train models, run evaluations, and analyze data yourself, both to deliver impact directly and to learn what's worth building to multiply the team's work. The bar is someone who understands the end-to-end impact they intend to make, measures it from the outset, and would rather find out they were wrong in a week than in a quarter. That discipline is what turns cross-functional ownership into an advantage
Raise the team's experimental velocity — make it faster to launch an experiment job, get a number back you can trust, and know what to try next
Maintain and evolve our JAX training framework, keeping it scalable and efficient for large-scale distributed training runs on TPU
Improve the data our models learn from: investigating quality issues, building the tooling to surface them, and turning what you find into measurable accuracy gains
Analyze the accuracy of production models, build evaluation harnesses, and work out which improvements will matter most to customers
Translate research prototypes into production-ready systems, refactoring and modernizing model architectures and infrastructure along the way
Optimize production inference for speech language models, both from a serving architecture perspective and through advanced techniques such as quantization and speculative decoding
Investigate and resolve performance bottlenecks across the stack, from low-level kernels (XLA, Pallas) to high-level system design
Partner with researchers, infrastructure, and production engineering to trace problems to their real source and ship fixes that hold