Member of Technical Staff — ML Research, Multimodal

Causal Labs, San Francisco +1

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: 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

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)

Causal Labs, San Francisco +1

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

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)