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. Predicting the future is only half the battle; the other half is identifying the actions that can alter it. Your mission is to build the planning layer on top of the LPM — conditioning the model on objectives and producing the actions that achieve them, from operational decisions to physical interventions. It is the capability that provides our models with interventional causality rather than merely observational causality, and it has no established playbook.
Responsibilities
Research and implement methods that turn a predictive physics model into one that reasons toward objectives — planning, control, and decision-making against a learned model of the world
Develop approaches for decision-making under uncertainty in high-dimensional, continuous physical state spaces
Build interfaces for specifying objectives and constraints, and methods for producing actions that satisfy them
Run experiments and ablations that connect reasoning methods to decision quality
Work across the full ML stack — data, model, eval, and infrastructure — to take ideas from prototype to scaled training runs
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 area (e.g. reinforcement learning, planning and control, decision-making under uncertainty, model-based RL, post-training of large models)
Experience training models and the ability to understand experimental results through careful analysis and ablation studies
Familiarity with the challenges of reasoning, planning, or acting with learned models
A track record of turning open-ended research problems into working systems