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Research Fellowship - Mechanistic Interpretability at V max | LAYIQ

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

Research Fellowship - Mechanistic Interpretability

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