Member of Technical Staff - Mechanistic Interpretability

V_max 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.

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. 

• 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.
• Investigate how internal representations evolve during RL and post-training, and use these insights to improve training objectives.
• Develop infrastructure for reproducible, large-scale experiments on LLM agents, interpretability tools, and RL environments.
• Define and pursue a high-impact research agenda that advances Vmax’s goal of open-ended learning beyond imitation of human expertise.

• PhD or equivalent experience in machine learning, reinforcement learning, or a closely related field.
• Track record of research excellence, as demonstrated by publications, open source work, deployed AI systems, or other substantial technical contributions.
• Deep understanding of modern machine learning, especially reinforcement learning, representation learning, and large language models.
• Strong familiarity with LLM post-training methods
• Experience designing and running rigorous ML experiments, including ablations, baselines, evaluation design, and failure analysis.
• Expertise with Python and at least one major ML framework such as PyTorch or JAX.
• Ability to work independently on open-ended research problems and turn ambiguous ideas into concrete experimental programs.

• Experience with mechanistic interpretability techniques such as activation patching, probing, sparse autoencoders, feature attribution
• Experience training or evaluating language-model agents in interactive, tool-using, or multi-step reasoning settings.
• Familiarity with scalable RL infrastructure, distributed training, experiment tracking, and large-scale evaluation pipelines.
• Experience developing reward models, verifiers, process supervision methods, or automated evaluation systems.
• Demonstrated software engineering ability, especially in research codebases that require reliability, reproducibility, and iteration speed.
• Ability to present technical results and their strategic implications to both research and non-research audiences.