Member of Technical Staff — ML Research, Interpretability
On-siteSan Francisco, California, United States
Job Summary
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. This role supports the development of a Large Physics foundation Model designed to predict and alter physical systems, focusing on causal intelligence and interpretability.
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
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