Member of Technical Staff - Mechanistic Interpretability
About Vmax
Vmax is an applied research lab developing AI capable of open-ended learning. We are building systems to exceed humans in all capacities by optimising beyond the local maxima of learning from human expertise.
About the role
LLMs are fantastically powerful and there is a rapidly growing corpus of work devoted to understanding their internal representations and computations. We use the tools of mechanistic interpretability to enhance reinforcement learning by generating intrinsic rewards as a supplement or alternative to downstream human-generated verifiers.
Responsibilities
Develop methods for using mechanistic interpretability to extract useful training signals from the internal states of language models.
Turn representations, features, circuits, and causal model behaviors into intrinsic rewards for reinforcement learning.
Compare interpretability-derived rewards against human feedback, learned reward models, verifiers, and task-level outcome rewards.
Design metrics and baselines for reward quality, including alignment with intended behavior, generalization across tasks, robustness, and resistance to reward hacking.
