Causal
Member of Technical Staff — Research, Physics
San Francisco · Staff+
Sponsorship not specifiedDetected 1 day ago
Machine LearningCFDRoboticsResearchProblem SolvingThermodynamics
About the role
- 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.
Responsibilities
- Develop evaluations that test whether the model's behavior is physically coherent, not just statistically accurate
- Partner with model, evaluation, and interpretability teams to connect physical understanding to research direction
- Ability to collaborate closely with ML researchers and translate physical principles into technical requirements
Requirements
- 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
- We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
Company info
- What we're looking for
This listing is sourced directly from Causal's careers page and normalized into a canonical job model.