# Research Fellowship - Mechanistic Interpretability at Vmax AI Corp

- Company: Vmax AI Corp
- Location: San Francisco, CA
- Type: Internship · Internship
- Posted: 2026-05-20
- Apply: https://jobs.ashbyhq.com/vmax/7344d5fd-6fa3-4bce-b7bd-e73aa4dd568b
- Page: https://newgrad.ai/jobs/research-fellowship-mechanistic-interpretability-8b92cb87

## Summary by newgrad.ai

What you'll do:

- Develop mechanistic interpretability methods for understanding language models and agents' internal representations and computations.
- Investigate how model internals generate intrinsic rewards and training signals for reinforcement learning systems.
- Design experiments testing whether interpretability-derived signals improve learning, exploration, generalization, and sample efficiency.
- Build research code and evaluation infrastructure while communicating progress through written updates and presentations.

What they're looking for:

- Currently enrolled in PhD program in machine learning, computer science, AI, or related technical field.
- Track record of research excellence demonstrated through publications, preprints, or technical projects.
- Working understanding of reinforcement learning and familiarity with mechanistic interpretability methods.
- Strong Python programming and experience with PyTorch or JAX.

## Posting

### 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

From Vmax AI Corp's public posting, laid out for reading by newgrad.ai with the wording unchanged. Check the original before you apply.

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More new-grad and intern roles: https://newgrad.ai/llms.txt