Impress employers and recruiters.
Choose from hundreds of resume examples.

Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

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.

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.

Skills

• LLM post-training methods
• Intrinsic motivation
• Unsupervised RL
• Auxiliary objectives
• Representation learning for RL
• Curiosity-driven learning
• Scalable ML experimentation
• Distributed training
• Experiment tracking
• Reproducible research infrastructure
• Turning mechanistic understanding into practical training methods

Location

• San Francisco

Work Type

• Hybrid

Experience Level

• PhD students or equivalent early-career researchers

Education Level

• PhD

About the Company

• 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.