Member of Technical Staff — ML Research, Multimodal

We look for researchers who are excited to tackle unsolved problems. Predicting how physical systems evolve means learning from observations that language and vision models were not built for — sparse sensors, point clouds, hyperspectral imagery, physical fields — at a scale that dwarfs what is used to train even today's frontier LLMs. Your mission is to design the architectures and training recipes that turn these multimodal observations into a model that predicts the future of the physical world.

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 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, 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