Research Fellowship - Open Endedness

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

A core focus of ours is agents that can learn to find their own objectives in the world. We are looking for researchers to design and build new ways of using RL where the formulation of rewards and tasks need to be discovered, rather than given.

This 3 to 6 month fellowship is for PhD students or equivalent early-career researchers who want to work on LLMs that can learn in open-ended settings. You will own a focused research project, work closely with Vmax technical staff, and contribute to research publications.

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.

Role Requirements

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.

Nice to have

Experience with LLM post-training methods

Experience with scalable ML experimentation, distributed training, experiment tracking, or reproducible research infrastructure.

Demonstrated taste for identifying non-obvious research directions and converting them into tractable experiments.

Role specific location policy

This role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement