MemberofTechnicalStaff-RLAlgorithms

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 RL has become the de-facto method of post-training LLMs. We are limited by the sample efficiency of the current policy gradient algorithms in use today, and are looking for a talented researcher to weave together pre-LLM and post-LLM approaches to learning from experience. Responsibilities Develop new RL algorithms for post-training language models. Adapt ideas from pre-LLM reinforcement learning, such as model-based RL, temporal abstraction, and value-based learning, to modern LLM and agentic settings. Establish empirical baselines and evaluation protocols for measuring sample efficiency, robustness, generalization, and reward exploitation in LLM RL. Analyze failure modes of RL-trained models, including reward hacking, mode collapse, over-optimization, exploration failures, and distribution shift. Collaborate with researchers working on environments, evals, interpretability, reward modeling, and infrastructure to turn algorithmic ideas into reliable training systems. Own and develop a research agenda within Vmax, from identifying promising directions to executing experiments and communicating results. 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. Experience with large-scale ML infrastructure, dist