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

# Member of Technical Staff - RL Infrastructure at Vmax

- **Company:** Vmax
- **Location:** San Francisco, CA
- **Posted:** 2026-05-20
- **Last confirmed live:** 2026-09-10
- **Apply:** https://job-boards.greenhouse.io/vmax/jobs/4253287009

## 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 strong infrastructure engineers who can build the systems layer for RL at scale: distributed rollouts, training orchestration, inference, evals, data pipelines, observability, and reliability. You will create the durable platform that enables researchers and applied ML engineers to run, debug, and reproduce large-scale RL experiments.

## Responsibilities

- Build infrastructure for distributed RL training and inference across thousands of GPUs
- Improve the reliability, debuggability, and throughput of RL experiments.
- Build interfaces that allow researchers and applied ML engineers to launch, inspect, compare, and reproduce experiments easily.
- Own infrastructure projects end to end, from architecture and implementation through deployment, documentation, and long-term maintenance.
- Identify and eliminate bottlenecks in training, rollout generation, eval execution, data movement, and cluster utilization.
- Maintain engineering standards for RL infrastructure, including testing, observability, versioning, and reproducibility.

## Minimum Requirements

- Strong software engineering experience.
- Experience building infrastructure for LLM inference and/or RL training.
- Experience with GPU clusters, distributed training, model serving, or high-throughput inference systems.
- Familiarity with vLLM, SGLang and modern LLM-RL training frameworks
- Strong understanding of system reliability, observability, testing, debugging, and performance optimization.
- Ability to work closely with ML researchers and translate messy experimental workflows into durable infrastructure.
- Experience building tools, platforms, or services used by other technical users.
- Strong judgment around technical tradeoffs: when to prototype, when to harden, when to simplify, and when to redesign.
- Clear written and verbal communication, especially around system design, operational risks, and engineering tradeoffs.

## Nice to have

- Experience supporting research teams or fast-moving ML teams.
- Experience at a high engineering bar organization where reliability, ownership, and code quality were central.
- Evidence of strong independent technical work, such as open-source projects, infrastructure projects, competitions, or substantial systems built from scratch.
- 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 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
- [Member of Technical Staff - Open Endedness](https://feeny.ai/job/member-of-technical-staff-open-endedness-vmax-san-francisco-6zfvz7zqa58g) — San Francisco, CA