# Member of Technical Staff - Mechanistic Interpretability - Company: [Vmax]() - Location: SF - Team: Research and Engineering - Employment type: Full Time - Salary: $300K – $500K - Posted: May 20, 2026 ## Job description ## **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. * 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. ## Minimum Requirements * 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. ## Nice to have * 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. ## **Role specific location policy** * This role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement ## Compensation The expected salary range for this position is $300,000 - $500,000 USD ## Apply [Apply on Vmax]() Canonical job page: