Causal
Member of Technical Staff — ML Research, Planning
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 researchers who are excited to tackle unsolved problems.
- Predicting the future is only half the battle; the other half is identifying the actions that can alter it.
Responsibilities
- Research and implement methods that turn a predictive physics model into one that reasons toward objectives - planning, control, and decision-making against a learned model of the world
- Develop approaches for decision-making under uncertainty in high-dimensional, continuous physical state spaces
- Build interfaces for specifying objectives and constraints, and methods for producing actions that satisfy them
Requirements
- We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
- Experience training models and the ability to understand experimental results through careful analysis and ablation studies
- Familiarity with the challenges of reasoning, planning, or acting with learned models
- A track record of turning open-ended research problems into working systems
- 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.