Research Fellow - Open Endedness
About the job
- 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.
- 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.
- 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.
About the company
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.
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
San Francisco, California
Founded
2025
Get referred to Vmax
See people who can refer or advise you
Simplify's Take
What believers are saying
- South Park Commons still lists Vmax active and hiring in August 2026.
- Vmax published unix-ctf and PROPEL in 2026, proving real research velocity.
- Hiring for RL Infrastructure and Applied RL suggests customer-ready systems work, not just theory.
What critics are saying
- Open-ended learning remains unproven commercially; research outputs do not equal a durable product.
- $300k-$500k roles in 2026 signal heavy burn before revenue arrives.
- OpenAI, Anthropic, and Nvidia can commoditize RL environment tooling, killing Vmax's moat.
What makes Vmax unique
- Vmax builds proprietary-data-to-RL-environment pipelines, not generic model fine-tuning.
- Its unix-ctf and PROPEL research show environment generation for long-horizon agents.
- Matthew Sargent and Augustine Mavor-Parker bring PhD-level RL depth from UCL, Redwood, and Illumina.
Help us improve and share your feedback! Did you find this helpful?