Responsibilities- Design and implement novel model architectures and training algorithms for learning from massive, multimodal physical data
- Solve core modeling problems unique to physical prediction: encoding heterogeneous and irregularly-sampled modalities, stable long-horizon rollouts, and probabilistic forecasting
- Run experiments and ablations that connect modeling and data decisions to predictive skill, including which data sources and mixtures most improve the model
- Work across the full ML stack - data, model, eval, and infrastructure - to take ideas from prototype to scaled training runs
- Stay up-to-date on research to bring new ideas to work
What we're looking forWe value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
- Strong grasp of machine learning fundamentals, with depth in at least one relevant domain (e.g. sequence or world models, computer vision, sensor fusion, generative modeling, physics-informed NNs)
- Experience training large-scale models and the ability to understand experimental results through careful analysis and ablation studies
- Familiarity with distributed training and the systems considerations of scaling models
- A track record of turning open-ended research problems into production models
