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 forWe 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
