Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.
We look for researchers who are excited to tackle unsolved problems. We're training powerful models grounded in observable feedback and verifiable ground truth, leveraging our experience in frontier research and training large-scale models from scratch across relevant fields like language, vision, robotics, biology, materials, physics, and weather.
Responsibilities
- Work across the full ML stack (data, model, eval, and infrastructure)
- Implement novel model architectures and training algorithms
- Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets
- Rapidly iterate on experiments and ablations
- Stay up-to-date on research to bring new ideas to work
What we’re looking for
- Strong grasp of machine learning fundamentals, and depth in at least one core domain (e.g. Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs)
- Experience training models and an ability to understand experiment results through careful analysis and ablation studies.
- Experienced at writing and optimizing massive petabyte-scale data pipelines.
- Familiarity with distributed training and inference.