Member of Technical Staff — ML Research, Interpretability
Causal · San Francisco
About this role
## Member of Technical Staff — ML Research, Interpretability ### About the mission We’re building **general causal intelligence**—AI that can (1) **predict the future** and (2) **identify actions to alter it**. Our approach is to develop a **Large Physics foundation model (LPM)**, since physical systems are governed by **verifiable cause and effect**. We’re looking for researchers who are excited to tackle unsolved problems in **causal structure learning** and **interpretability**—so we can understand what the model has actually learned before anyone acts on its predictions or recommended interventions. ### Your mission **Open the model up** to understand: - its **internal representations** - how it **explains its outputs** - how to build the **trust** required to act on physical systems ### Responsibilities - 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** ### What we’re looking for We value a relentless approach to problem-solving, **rapid execution**, and the ability to quickly learn in unfamiliar domains. - 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** ### Background / context Our founding team has built and deployed AI against the physical world in **robotics, drug discovery, and particle physics** at institutions including **DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN**.
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