Open Application – Exceptional Talent at Vmax (San Francisco, CA)

Vmax· San Francisco, CA·3d ago

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 optimizing beyond the local maxima of learning from human expertise.

About the role

Vmax is always interested in exceptional people who can help us build AI systems capable of open-ended learning. This application is for candidates whose strongest contribution may not fit neatly into one of our current roles, but who have clear evidence of unusually strong ability, agency, and or ambition.

Why work at Vmax

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.

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.

Sources

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