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About the Role

We are building a Large Physics foundation Model (LPM) to achieve general causal intelligence, capable of predicting the future and identifying actions to alter it. Our mission is to ensure the model evolves towards this thesis: grounded in physical law, evaluated against it, and ready to generalize across domains. We seek researchers excited to tackle unsolved problems, focusing on understanding the model's internal representations, explaining its outputs, and building trust for acting on physical systems.

Responsibilities

Probe the model's internal representations for physical quantities, structure, and conservation laws
Develop methods to explain individual predictions and the model's reasoning about interventions
Investigate whether interventions in the model's internal state produce physically coherent responses
Build tools and techniques for debugging model failures and understanding rollout behavior
Partner with model, evaluation, and domain teams to turn interpretability findings into better models and greater trust

Requirements

Relentless approach to problem-solving, rapid execution, and ability to quickly learn in unfamiliar domains
Strong grasp of machine learning fundamentals and the internals of modern neural network architectures
Experience or strong interest in interpretability, representation analysis, or related research
Strong engineering skills for building interpretability tooling and running careful experiments
Rigorous, hypothesis-driven approach to understanding model behavior
Track record of turning open-ended research questions into concrete findings

About the Company

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.