# Member of Technical Staff - Mechanistic Interpretability at Vmax

> United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Avmax%3A4251680009 (read 2026-11-07).

## Facts (observed)

- Company: [Vmax](https://alderhq.app/companies/vmax)
- Location text: San Francisco; SF
- Where it can be done from (inferred): United States (evidence: "Location: San Francisco; SF")
- Posted: 2026-05-20 (171 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-03
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/vmax/jobs/4251680009
- Skills listed (inferred): Python, PyTorch, LLMs

## Pay and sponsorship

- Posted pay: $300k–$500k per year (from the description)
- Market band for similar posted roles (SF Bay Area, n 969): p25 $205k, median $240k, p75 $284k

## Description (the employer's text)

### 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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Avmax%3A4251680009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.