Member of Technical Staff – Research in Physics

About the role

We are seeking a Member of Technical Staff to help build our Large Physics foundation Model (LPM). The role focuses on grounding AI in physical laws to enable causal intelligence for predicting and controlling complex systems such as weather.

Key responsibilities

• Assess model consistency with conservation laws and physical constraints, and evaluate the impact of physics‑informed inductive biases.
• Design and run evaluations that test physical coherence beyond statistical accuracy.
• Advise on the physics of modeled systems—from fluid dynamics to thermodynamics—and guide their numerical treatment.
• Investigate cross‑domain generalization of the LPM and identify failure modes.
• Collaborate with model, evaluation, and interpretability teams to align physical understanding with research direction.

Required profile

• Deep expertise in physics (fluid dynamics, thermodynamics, computational physics) typically demonstrated by a PhD or equivalent research experience.
• Experience with numerical simulation of physical systems (e.g., CFD) and awareness of its trade‑offs.
• Strong interest at the intersection of machine learning and physical modeling.
• Ability to work closely with ML researchers and translate physical principles into technical requirements.
• Evidence‑driven, rigorous approach to evaluating model quality.

Required skills

• Fluid dynamics
• Thermodynamics
• Computational physics
• Numerical simulation
• Computational Fluid Dynamics (CFD)
• Machine learning