Member of Technical Staff — ML Research, Interpretability

The role

This person will probe model internal representations for physical quantities, structure, and conservation laws. They will develop methods to explain predictions and reasoning about interventions to build trust in physical systems.

What you will do

• Probe the model's internal representations for physical quantities, structure, and conservation laws
• Develop methods to explain individual predictions and the model's reasoning about interventions
• Investigate whether interventions in the model's internal state produce physically coherent responses
• 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

Required qualifications

• Strong grasp of machine learning fundamentals and the internals of modern neural network architectures
• Experience or strong interest in interpretability, representation analysis, or related research
• Strong engineering skills for building interpretability tooling and running careful experiments
• A rigorous, hypothesis-driven approach to understanding model behavior
• A track record of turning open-ended research questions into concrete findings

About the company

Causal is building a Large Physics foundation Model to achieve general causal intelligence. The founding team has built and deployed AI at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.