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About the Role
We are building a Large Physics foundation Model (LPM) to achieve general causal intelligence, enabling AI to predict the future and identify actions to alter it. This role focuses on bringing discipline to our reasoning research, defining objectives for our models, and evaluating decision quality in high-stakes operational environments.
Responsibilities
- Formulate objectives, constraints, and decision problems for reasoning models.
- Develop methodology for evaluating decision quality under uncertainty, including counterfactual reasoning.
- Translate complex operational environments into well-posed optimization and decision problems.
- Bring rigor to the validation of optimization and decision-making models for real-world use.
- Collaborate with reasoning, evaluation, and product teams to connect research to informed decisions.
Requirements
- Deep expertise in operations research, decision science, or a closely related field (typically a PhD or equivalent experience).
- Strong grasp of optimization and decision-making under uncertainty, ideally including stochastic methods.
- Experience in high-stakes operational settings where forecasts drive consequential decisions.
- Particular strength in evaluating the quality of optimization or decision models, not just building them.
- Ability to collaborate closely with ML researchers and translate operational realities into technical problems.
Skills
- Operations research
- Decision science
- Optimization
- Decision-making under uncertainty
- Stochastic methods
- Model evaluation
- Counterfactual reasoning
- Machine learning research collaboration
Experience Level
- PhD or equivalent experience
Education Level
- PhD
About the Company
- Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
- We are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect.
- We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
- 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.