Causal
Member of Technical Staff — ML Research, Interpretability
San Francisco · Staff+
Sponsorship not specifiedDetected 1 day 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.
- Our thesis is that scaling on physics yields a model capable of understanding the causal structure to predict and alter the future.
Responsibilities
- Develop methods to explain individual predictions and the model's reasoning about interventions
- Build tools and techniques for debugging model failures and understanding rollout behavior
- Partner with model, evaluation, and domain teams to turn interpretability findings into better models and greater trust
- Strong engineering skills for building interpretability tooling and running careful experiments
- to understand its internal representations, explain its outputs, and build the trust that acting on physical systems demands.
Requirements
- We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
- Experience or strong interest in interpretability, representation analysis, or related research
- A track record of turning open-ended research questions into concrete findings
- We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
Benefits
- Strong grasp of machine learning fundamentals and the internals of modern neural network architectures
Company info
- What we're looking for
This listing is sourced directly from Causal's careers page and normalized into a canonical job model.