Member of Technical Staff — ML Research, Multimodal
On-siteSan Francisco, California, United States
Job Summary
Design and implement novel model architectures and training algorithms for learning from massive, multimodal physical data, specifically encoding heterogeneous modalities and enabling stable long-horizon rollouts. Solve core modeling problems unique to physical prediction and run experiments connecting data decisions to predictive skill. 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. This role supports the development of a Large Physics foundation Model to predict and control physical systems, building on a team with experience in robotics, drug discovery, and particle physics.
Required Qualifications
- Strong grasp of machine learning fundamentals
- 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
- Ability to understand experimental results through careful analysis and ablation studies
- Familiarity with distributed training
- Familiarity with the systems considerations of scaling models
- A track record of turning open-ended research problems into production models
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