Member of Technical Staff — ML Research, Interpretability at causal
causal is hiring a Member of Technical Staff — ML Research, Interpretability in San Francisco, CA, US. On-site.
About causal
Causal Labs is building a Large Physics foundation Model (LPM) that aims to learn causality through physics and weather, which it describes as the most well-observed and consequential physical system. The company's focus is on advancing AI by understanding how actions influence the world.
Member of Technical Staff — ML Research, Interpretability job description
Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
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. Your mission is to ensure the model evolves towards this thesis: grounded in physical law, evaluated against it, and ready to generalize across domains.
We look for researchers who are excited to tackle unsolved problems. Our mission is to build models capable of learning the underlying causal structures of physical systems and interpretability is how we will know whether they have. Before anyone acts on a model's prediction or its recommended intervention, we need to understand what the model has actually learned. Your mission is to open the model up: to understand its internal representations, explain its outputs, and build the trust that acting on physical systems demands.
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
