About the role
Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
To achieve this breakthrough, 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.
We look for domain experts who are excited to tackle unsolved problems. A prediction matters most when it leads to better decisions — and evaluating decision quality in high-stakes operational environments is a challenge on its own. Your mission is to bring that discipline to our reasoning research: defining the objectives our models optimize toward and the methods by which we judge whether their decisions are actually good.
Responsibilities
* Formulate the objectives, constraints, and decision problems that our reasoning models optimize toward
* Develop methodology for evaluating decision quality under uncertainty, including counterfactual reasoning about outcomes
* Translate the realities of complex operational environments into well-posed optimization and decision problems
* Bring rigor to how optimization and decision-making models are validated for real-world use
* Partner with reasoning, evaluation, and product teams to connect research to the decisions it ultimately informs
What we're looking for
We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
* 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