Member of Technical Staff
Causal Labs
Member of Technical Staff
Causal Labs
Trains physics AI models to forecast weather
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 domain experts who are excited to tackle unsolved problems. Our thesis is that scaling on physics yields a model capable of understanding the causal structure to predict and alter the future. Your mission is to ensure the model evolves towards this thesis: grounded in physical law, evaluated against it, and ready to generalize across domains.
Responsibilities
Bring physical principles to bear on the model — assessing consistency with conservation laws and physical constraints, and where physics-informed inductive biases help or hinder
Develop evaluations that test whether the model's behavior is physically coherent, not just statistically accurate
Advise on the physics of the systems we model, from fluid dynamics to thermodynamics, and their numerical treatment
Investigate where the LPM generalizes across physical domains and where it breaks down
Partner with model, evaluation, and interpretability teams to connect physical understanding to research direction
What we're looking for
We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
Deep expertise in physics — fluid dynamics, thermodynamics, computational physics, or a closely related field (typically a PhD or equivalent research experience)
Familiarity with numerical simulation of physical systems (e.g. CFD) and its trade-offs
Interest in where machine learning and physical modeling meet
Ability to collaborate closely with ML researchers and translate physical principles into technical requirements
A rigorous, evidence-driven approach to evaluating model quality
Trains physics AI models to forecast weather
Causal Labs is an AI research company building a foundation model of physical systems for industries whose operations turn on the weather. The company trains on atmospheric sensor and observation data rather than text or images, a domain where cause and effect are governed by physics and yesterday's prediction can be scored against what happened. It runs early pilots with commercial partners and pairs researchers with engineers who deploy the models on customer data. Unlike conventional forecasters, which report what conditions are likely, it is also trying to identify which interventions would change an outcome. The company's goal is to make AI reason about cause and effect by grounding it in the most heavily observed physical system available.
Explore more opportunities at Causal Labs