---
title: 'Member of Technical Staff - Open Endedness at Vmax'
canonical: 'https://feeny.ai/job/member-of-technical-staff-open-endedness-vmax-san-francisco-6zfvz7zqa58g'
type: 'job'
last_seen: '2026-09-10'
---

# Member of Technical Staff - Open Endedness at Vmax

- **Company:** Vmax
- **Location:** San Francisco, CA
- **Posted:** 2025-11-05
- **Last confirmed live:** 2026-09-10
- **Apply:** https://job-boards.greenhouse.io/vmax/jobs/4056500009

## Job description

## 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.

## 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.

## Minimum Requirements

- PhD or equivalent experience in machine learning, reinforcement learning, artificial intelligence, or a closely related field.
- Track record of strong technical work, demonstrated through publications, open-source projects, deployed systems, competitions, or equivalent contributions.
- Deep understanding of reinforcement learning
- Strong interest in open-ended learning
- Experience with LLM post-training
- Strong empirical research ability, including designing experiments, choosing meaningful baselines, running ablations, and diagnosing unexpected results.
- Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.
- Ability to work independently on ambiguous research problems and turn high-level ideas into concrete experimental programs.
- Ability to collaborate effectively with researchers and engineers on ambiguous, fast-moving technical problems.
- Clear written and verbal communication of technical ideas, results, tradeoffs, and risks.

## Nice to have

- Experience with open-ended learning, automatic curriculum generation, intrinsic motivation, self-play, goal-conditioned RL, unsupervised skill discovery, multi-agent RL, quality-diversity, or evolutionary methods.
- Familiarity with methods such as POET,  population-based training, or multi-agent RL
- Experience designing benchmarks or evals for generalization, exploration, long-horizon learning or behavioral diversity
- 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

## Compensation

The expected salary range for this position is $300,000 - $500,000 USD

## About Vmax

## Company Overview
- **One-liner**: Vmax is an applied research lab developing AI capable of open-ended learning by building reinforcement learning systems that exceed human performance across every domain.
- **Entity Type**: Private (funding stage not disclosed)
- **Headquarters**: San Francisco, California, United States
- **Founded**: Not publicly available
- **Founders**: Augustine Mavor-Parker (Co-Founder, CTO) and Matthew Sargent (Co-Founder, CEO)

## Core Business
- **Primary Industry**: Artificial Intelligence / Reinforcement Learning Research
- **Target Customers**: B2B (enterprise AI teams, research institutions) and the broader AI research community (open-source tools)
- **Mission**: To build systems that exceed humans in all capacities by optimizing beyond the local maxima of learning from human expertise, discovering radically new ways of working rather than simply replacing human labor.

## Products & Services
- **Open-Ended Learning Research**: Core R&D in reinforcement learning algorithms and systems that allow agents to define and optimize their own goals, moving beyond human-curated task distributions.
- **Harbor**: Open-source LLM post-training framework designed for running RL environments and scaling post-training experiments.
- **ARES (Agentic Research and Evaluation Suite)**: Open-source evaluation harness for agentic research, enabling rigorous benchmarking of RL-based agents.
- **Unix-CTF & PopuLoRA**: Procedural environments and co-evolution methods for RL self-play, published as research artifacts.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Team size of 7 employees (as of mid-2026), with monthly headcount growth of +11.1%
- **Notable Investors/Partners**: No investors publicly listed; alumni from top labs (DeepMind, Meta, The Alan Turing Institute, Mila) have joined the team.
- **Growth Signals**: Active hiring (3 open positions as of July 2026), steady LinkedIn follower growth (+11.8% monthly), recent high-traction hires from Meta and Google DeepMind, and public research releases (May 2026).

## Competitive Advantages
- **Unconstrained Learning Approach**: Vmax avoids the local maxima of human-curated tasks by enabling agents to define their own goals — a true open-ended learning paradigm.
- **Research-to-Engineering Pipeline**: Tight coupling between RL research and production infrastructure (e.g., Harbor framework) allows fast iteration from theory to working systems.
- **Talent Density**: Small team (7 people) composed of specialists from top AI organizations, creating a high-expertise, low-bureaucracy environment.

## Strategic Focus
- **Post-Training Innovation**: Shifting LLM post-training from human-curated distributions to automatically generated, agent-defined tasks.
- **Infrastructure for RL Scaling**: Building reusable, reliable RL training, evaluation, and monitoring systems (Harbor, ARES) to support rapid experimentation.
- **Mechanistic Interpretability for RL**: Exploring how internal model representations can generate intrinsic rewards to improve learning and robustness.
- **Publication & Open-Source**: Regularly releasing research (e.g., PROPEL, Unix-CTF, PopuLoRA) and code to attract talent and establish thought leadership.

## Why Work Here
- **Compensation & Equity**: Salary for MTS roles ranges from $300,000 - $500,000 USD (plus likely equity/benefits).
- **Work Arrangement**: Primarily San Francisco office-based; hybrid considered for exceptional candidates.
- **Culture & Impact**: Small, high-autonomy team with no red tape; engineers own ambiguous projects end-to-end, from problem framing to production.
- **Research Focus**: Direct contribution to cutting-edge RL research with real-world deployment; access to top compute and data.
- **Notable Perks**: Opportunity to publish, attend conferences, and work alongside former DeepMind, Meta, and Alan Turing Institute researchers. Strong emphasis on engineering quality and reproducibility.

## Sources
1. [vmax.ai](https://vmax.ai/)
2. [LinkedIn - Vmax](https://www.linkedin.com/company/vmax-ai)
3. [Greenhouse - MTS Applied RL](https://job-boards.greenhouse.io/vmax/jobs/4056502009)
4. [Greenhouse - Research Fellowship](https://job-boards.greenhouse.io/vmax/jobs/4253430009)
5. [GitHub - VmaxAI](https://github.com/VmaxAI)

## Other roles at Vmax

- [Member of Technical Staff - RL Infrastructure](https://feeny.ai/job/member-of-technical-staff-rl-infrastructure-vmax-san-francisco-qwykrpnbfwgm) — San Francisco, CA
- [Member of Technical Staff - RL Algorithms](https://feeny.ai/job/member-of-technical-staff-rl-algorithms-vmax-san-francisco-fd2386sg75px) — San Francisco, CA
- [Member of Technical Staff - Mechanistic Interpretability](https://feeny.ai/job/member-of-technical-staff-mechanistic-interpretability-vmax-san-francisco-f9q03m76tmdt) — San Francisco, CA
- [Research Fellowship - Mechanistic Interpretability](https://feeny.ai/job/research-fellowship-mechanistic-interpretability-vmax-san-francisco-3wdzvbm4d8bc) — San Francisco, CA
- [Open Application – Exceptional Talent](https://feeny.ai/job/open-application-exceptional-talent-vmax-san-francisco-fj7yd501htx8) — San Francisco, CA
- [Research Fellowship - Open Endedness](https://feeny.ai/job/research-fellowship-open-endedness-vmax-san-francisco-cskvdpnjtjay) — San Francisco, CA
- [Member of Technical Staff - Applied RL](https://feeny.ai/job/member-of-technical-staff-applied-rl-vmax-san-francisco-151f2rpje236) — San Francisco, CA