Member of Technical Staff — Research, Physics
Causal Labs· Research
San Francisco, SingaporeFull-timePosted 2mo ago
About the role
Role focused on integrating physical principles with a Large Physics foundation Model (LPM), ensuring models are grounded in physical law, evaluated for physical coherence, and capable of generalizing across domains, starting with weather.
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
Qualifications
- 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
Skills
Required:Physics expertise (fluid dynamics, thermodynamics, computational physics)Numerical simulation (CFD)ML and physical modeling integrationResearch evaluation
Last updated Oct 2, 2026. Interested? Apply on the company site.
About Causal Labs
Towards general causal intelligence
Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather
causalityphysicsweatherAIfoundation model
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