# Research Fellowship - Mechanistic Interpretability at Vmax

> United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Avmax%3A4253430009 (read 2026-11-06).

## 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 (170 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/4253430009
- Skills listed (inferred): Python, PyTorch, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 1630): p25 $161k, median $200k, p75 $242k

## 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 optimizing 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.

This 3 to 6 month fellowship is for PhD students or equivalent early-career researchers who want to work at the intersection of mechanistic interpretability and reinforcement learning. You will own a focused research project, work closely with Vmax technical staff, and contribute to research publications.

### Responsibilities

- Develop mechanistic interpretability methods for understanding internal representations, features, circuits, and computations in language models and agents.
- Investigate how model internals can be used to generate intrinsic rewards, auxiliary objectives, diagnostics, or training signals for reinforcement learning.
- Design and run experiments that test whether interpretability-derived signals improve learning, exploration, generalization, robustness, or sample efficiency.
- Compare internally derived rewards against baselines such as human-generated verifiers, reward models, task-level outcome rewards, and standard intrinsic motivation methods.
- Use techniques such as probing, activation analysis, sparse autoencoders, causal interventions, feature attribution, or representation analysis to study model behavior.
- Analyze failure modes, including reward hacking, spurious features, non-causal correlations, objective misspecification, and overfitting to narrow evaluation distributions.
- Build research code, evaluation harnesses, and experimental infrastructure that make results reproducible and useful to the broader team.
- Communicate research progress clearly through written updates, internal presentations, and final project outputs.
### Role Requirements

- Currently enrolled in a PhD program in machine learning, computer science, artificial intelligence, computational neuroscience, mathematics, or a related technical field. Exceptional candidates with equivalent research experience may also be considered.
- Track record of research excellence or strong research promise, demonstrated through publications, preprints, open-source work, technical projects, competitions, or publicly available artifacts.
- Working understanding of reinforcement learning.
- Familiarity with mechanistic interpretability, representation analysis, or empirical methods for understanding neural networks.
- Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.
- Clear written and verbal communication of technical ideas.
### Nice to have

- Experience with LLM post-training methods
- Familiarity with intrinsic motivation, unsupervised RL, auxiliary objectives, representation learning for RL, or curiosity-driven learning.
- Experience with scalable ML experimentation, distributed training, experiment tracking, or reproducible research infrastructure.
- Interest in turning mechanistic understanding into practical training methods, rather than only analyzing models after training.
### Role specific location policy

- This role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Avmax%3A4253430009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.