# Member of Technical Staff - Mechanistic Interpretability at Vmax AI Corp

Member of Technical Staff - Mechanistic Interpretability at Vmax AI Corp in SF. Full-time, paying $300k–$500k. Posted May 20, 2026.

- Company: Vmax AI Corp
- Location: SF
- Job type: Full-time
- Pay: $300k–$500k
- Posted: 2026-05-20
- Apply: https://jobs.ashbyhq.com/vmax/ab802926-2f15-474a-b6e0-b64430ee6cab/application
- Source: https://jobmagic.ai/jobs/member-of-technical-staff-mechanistic-interpretability-vmax-ai-corp-tfxcq4

## 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.

## 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