---
title: 'Member of Technical Staff - Applied RL at Vmax'
canonical: 'https://feeny.ai/job/member-of-technical-staff-applied-rl-vmax-san-francisco-151f2rpje236'
type: 'job'
last_seen: '2026-09-10'
---

# Member of Technical Staff - Applied RL 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/4056502009

## 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

This role is for exceptional ML engineers who can turn RL research ideas into working training systems, evals, environment and rewards. You will work across research and engineering to make post-training methods reliable, measurable, and fast to iterate on.

## Responsibilities

- Build and improve RL training pipelines for language model based agents.
- Translate research ideas into working implementations, including reward functions, verifiers, environment interfaces, rollout pipelines, and evaluation harnesses.
- Design experiments that test whether RL methods are actually improving model behavior, sample efficiency, robustness, or generalization.
- Create quality monitoring tools for  RL experiments, including regression tests, eval suites, and reward-hacking checks.
- Debug unstable training runs, diagnose poor learning dynamics, and identify whether failures come from algorithms, rewards, data, infrastructure, or evals.
- Build 0→1 systems for new RL workflows, then harden them into reusable infrastructure.
- Improve the reliability, reproducibility, and speed of experimentation across RL projects.
- Own technically ambiguous projects end to end, from problem framing through implementation, evaluation, and iteration.

## Minimum Requirements

- Strong practical ML engineering ability, demonstrated through shipped systems, open-source projects, competitions, independent projects, or equivalent experience.
- Hands-on experience building, training, evaluating, or debugging ML systems.
- Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.
- Working understanding of reinforcement learning, supervised learning, optimization, and modern deep learning.
- Ability to independently take an ambiguous technical problem and drive it to a working implementation..
- Ability to collaborate closely with researchers while maintaining high engineering standards.
- Experience building systems that are reliable, maintainable, and usable by other technical team members.
- Clear written and verbal communication.

## Nice to have

- Experience supporting research teams or fast-moving ML teams.
- Expertise in building experiment tracking, evaluation platforms, dataset/versioning systems, or reproducibility infrastructure.
- Experience at a high engineering bar organization where reliability, ownership, and code quality were central.
- Experience reducing operational complexity in systems that had become brittle, slow, or hard to debug.

## 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 - Open Endedness](https://feeny.ai/job/member-of-technical-staff-open-endedness-vmax-san-francisco-6zfvz7zqa58g) — San Francisco, CA