Mechanistic Interpretability (llms) Machine Learning Expert

Responsibilities - Conduct mechanistic interpretability research on transformer-based and other neural network architectures - Identify, isolate, and analyze computational circuits responsible for specific model behaviors - Apply and extend techniques such as activation patching, probing, sparse autoencoders, and attention analysis - Develop tools and frameworks to automate or scale interpretability workflows across model families - Document methodologies, findings, and technical approaches clearly and reproducibly

Required qualifications - Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, or equivalent) - Master's or PhD in Machine Learning, Artificial Intelligence, Computer Science, or a related quantitative field - Demonstrated expertise in mechanistic interpretability, model analysis, or AI safety research - Deep familiarity with transformer architectures and modern large language model internals - Strong problem-solving skills and ability to work independently on open-ended research tasks

Preferred qualifications - Hands-on experience with interpretability tools and libraries (e.g., TransformerLens, baukit, or similar) - Familiarity with sparse autoencoders, superposition, and feature geometry research - Background in TA'ing or teaching deep learning, NLP, or AI safety courses

Why apply - Flexible Time Commitment – Work on your schedule while tackling meaningful research challenges - Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart - Exceptional Pay – Project-based pay ranges from $150–$200/hour - Portfolio Building – Gain experience on frontier interpretability and AI safety research problems - Professional Growth – Sharpen your skills on varied, challenging model analysis and reverse-engineering tasks

Employment type: Contract Time commitment: 10 hours/week Location: Remote