Causal
Member of Technical Staff — Research, Physics
San Francisco. Posted 3 months ago
The role
The person will ensure the Large Physics foundation Model is grounded in physical law and generalizes across domains. This involves bringing physical principles to the model and developing evaluations for physical coherence.
What you will do
- Bring physical principles to bear on the model
- Assess consistency with conservation laws and physical constraints
- Develop evaluations to test if model behavior is physically coherent
- Advise on the physics of modeled systems including fluid dynamics and thermodynamics
- Investigate where the LPM generalizes or breaks down across physical domains
- Partner with model, evaluation, and interpretability teams
Required qualifications
- Deep expertise in physics such as fluid dynamics, thermodynamics, computational physics, or a closely related field
- 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
About the company
Causal is building a Large Physics foundation Model to achieve general causal intelligence. The founding team has built and deployed AI in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.