Research Fellow
Vmax
Research Fellow
Vmax
Automates generation of reinforcement learning environments
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.
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.
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.
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.
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.
This role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement
Vmax.ai builds tools to automate reinforcement learning (RL) development. It creates scalable RL environments from proprietary data so engineers can train agents for long-horizon tasks without a lot of manual RL engineering. The core product concept is to automatically transform company data and evaluation metrics into reusable RL environments, enabling post-training of large language model–based agents for domain-specific use cases. The company distinguishes itself by combining automated environment design with data-driven RL environment generation, aiming to cut human intervention in RL workflows. The founding team’s deep RL background, stealth mode status, and backing from South Park Commons position it to pursue enterprise-grade RL automation and domain-specific AI through a platform that handles data-to-environment conversion and subsequent agent fine-tuning. Its goal is to scale RL development by reducing setup work and enabling long-horizon tasks across domains.
Explore more opportunities at Vmax