---
title: 'Member of Technical Staff - Mechanistic Interpretability at Vmax'
canonical: 'https://feeny.ai/job/member-of-technical-staff-mechanistic-interpretability-vmax-san-francisco-f9q03m76tmdt'
type: 'job'
last_seen: '2026-09-10'
---

# Member of Technical Staff - Mechanistic Interpretability 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/4251680009

## 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

LLMs are fantastically powerful and there is a rapidly growing corpus of work devoted to understanding their internal representations and computations. We use the tools of mechanistic interpretability to enhance reinforcement learning by generating intrinsic rewards as a supplement or alternative to downstream human-generated verifiers.

## Responsibilities

- Develop methods for using mechanistic interpretability to extract useful training signals from the internal states of language models.
- Turn representations, features, circuits, and causal model behaviors into intrinsic rewards for reinforcement learning.
- Compare interpretability-derived rewards against human feedback, learned reward models, verifiers, and task-level outcome rewards.
- Design metrics and baselines for reward quality, including alignment with intended behavior, generalization across tasks, robustness, and resistance to reward hacking.
- Investigate how internal representations evolve during RL and post-training, and use these insights to improve training objectives.
- Develop infrastructure for reproducible, large-scale experiments on LLM agents, interpretability tools, and RL environments.
- Define and pursue a high-impact research agenda that advances Vmax’s goal of open-ended learning beyond imitation of human expertise.

## Minimum Requirements

- PhD or equivalent experience in machine learning, reinforcement learning, or a closely related field.
- Track record of research excellence, as demonstrated by publications, open source work, deployed AI systems, or other substantial technical contributions.
- Deep understanding of modern machine learning, especially reinforcement learning, representation learning, and large language models.
- Strong familiarity with LLM post-training methods
- Experience designing and running rigorous ML experiments, including ablations, baselines, evaluation design, and failure analysis.
- Expertise with Python and at least one major ML framework such as PyTorch or JAX.
- Ability to work independently on open-ended research problems and turn ambiguous ideas into concrete experimental programs.

## Nice to have

- Experience with mechanistic interpretability techniques such as activation patching, probing, sparse autoencoders, feature attribution
- Experience training or evaluating language-model agents in interactive, tool-using, or multi-step reasoning settings.
- Familiarity with scalable RL infrastructure, distributed training, experiment tracking, and large-scale evaluation pipelines.
- Experience developing reward models, verifiers, process supervision methods, or automated evaluation systems.
- Demonstrated software engineering ability, especially in research codebases that require reliability, reproducibility, and iteration speed.
- Ability to present technical results and their strategic implications to both research and non-research audiences.

## 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
- [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