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Research Fellowship - Open Endedness

Vmax· Research and Engineering
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SFFull-timePosted 3mo ago

About the role

A 3 to 6 month fellowship for PhD students or early-career researchers to design and build RL approaches where rewards and tasks are discovered, focusing on LLMs that can learn in open-ended settings.

Responsibilities

  • Develop RL methods for agents that can discover useful objectives, tasks and curricula without relying entirely on human-specified rewards.
  • Design systems for open-ended learning, including unsupervised/automated environment design, asymmetric self-play, and intrinsic motivation.
  • Build training loops where agents learn from interaction, exploration, novelty, competence progress, self-generated challenges, or other nonstandard reward signals.
  • Investigate how agents can avoid collapse into trivial, degenerate, or easily exploitable objectives.
  • Own and develop a research agenda within Vmax, from identifying promising directions to executing experiments and communicating results.]
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Qualifications

  • Currently enrolled in a PhD program in machine learning, computer science, artificial intelligence, computational neuroscience, mathematics, or a related technical field. Exceptional candidates with equivalent research experience may also be considered.
  • Track record of research excellence or strong research promise, demonstrated through publications, preprints, open-source work, technical projects, competitions, or publicly available artifacts.
  • Working understanding of reinforcement learning.
  • Familiarity with unsupervised/automated environment design, asymmetric self-play, and/or intrinsic motivation.
  • Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.
  • Clear written and verbal communication of technical ideas.

Skills

Required:Reinforcement LearningUnsupervised Environment DesignPythonML Frameworks (PyTorch or JAX)Communication
Last updated Jul 5, 2026. Interested? Apply on the company site.
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About Vmax

Vmax is developing AI capable of open-ended learning. We build systems that exceed human performance across every domain, optimizing beyond the local maxima of learning from human expertise.

Vmax is automating reinforcement learning. We transform proprietary data and evals into new sets of environments. We refine agents on new examples of the tasks they are intended to perform.

AIagentsbessemer-ai-infrastructure-2026environmentsopen-ended learningreinforcement learningreinforcement-learning-platformsresearch
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