Work across the full ML stack (data, model, eval, and infrastructure)
Implement novel model architectures and training algorithms
Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets
Rapidly iterate on experiments and ablations
Stay up-to-date on research to bring new ideas to work

What we’re looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

Strong grasp of machine learning fundamentals, and depth in at least one core domain (e.g. Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs)
Experienced at training models and understanding experiment results through careful analysis and ablation studies.
Experienced at writing and optimizing massive petabyte-scale data pipelines.
Familiarity with distributed training.
[bonus] Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations.

You don’t have to meet every single requirement above.