Member of Technical Staff — ML Research, Interpretability

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.

• 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