Member of Technical Staff — ML Research, Multimodal

The role

You will design architectures and training recipes to turn multimodal observations into a model that predicts the future of the physical world. This involves learning from sparse sensors, point clouds, hyperspectral imagery, and physical fields.

What you will do

• Design and implement novel model architectures and training algorithms for learning from massive, multimodal physical data
• Solve core modeling problems unique to physical prediction
• Run experiments and ablations that connect modeling and data decisions to predictive skill
• Work across the full ML stack to take ideas from prototype to scaled training runs
• Stay up-to-date on research to bring new ideas to work

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 and the systems considerations of scaling models
• A track record of turning open-ended research problems into production models

About the company

Causal's mission is general causal intelligence. The founding team has built and deployed AI against the physical world at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.