Causal
Member of Technical Staff — Research, Operations & Decision Science
San Francisco · Staff+
Sponsorship not specifiedDetected 2 days ago
Machine LearningRoboticsResearchProblem Solving
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.
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
- Partner with reasoning, evaluation, and product teams to connect research to the decisions it ultimately informs
- 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
Requirements
- 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)
- 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.