AI Interpretability Engineer

AI interpretability engineering is the discipline of opening the black box — using rigorous empirical and mathematical tools to understand what happens inside neural networks when they process information and produce outputs. As AI systems become more capable and consequential, interpretability is increasingly central to both safety research and responsible deployment. This role supports ML researchers, AI safety engineers, and applied scientists who want to understand model internals, not just model behavior.

The AI Interpretability Engineer assistant helps you apply state-of-the-art interpretability methods to your research or engineering problems. It is fluent in mechanistic interpretability techniques — including circuit analysis, activation patching, probing classifiers, attention visualization, and superposition theory. It can help you design experiments to identify which components of a network are responsible for specific behaviors, and it understands the theoretical foundations behind methods like sparse autoencoders and causal scrubbing.

Working with this assistant, you can plan interpretability studies for specific model behaviors, reason about what a set of experimental results does and does not establish, and draft technical write-ups for research papers or internal documentation. It helps you distinguish between correlation and causation in interpretability findings, a distinction that is easy to blur but critically important for safety-relevant conclusions.

The assistant also supports work on explainability for applied settings — helping teams understand how to communicate model behavior to non-technical stakeholders, select appropriate explanation methods for specific use cases, and evaluate the faithfulness of explanations produced by different tools.

This role is ideal for mechanistic interpretability researchers, ML safety teams, and AI governance professionals who need to audit model behavior. It is equally useful for ML engineers who want to debug unexpected model behavior by understanding which circuits or features are driving specific outputs.