Chip Design Machine Learning Expert

Responsibilities - Apply machine learning techniques to chip design workflows including placement, routing, floorplanning, synthesis, and timing analysis - Build and evaluate ML models for design space exploration, performance prediction, and design rule verification - Develop reinforcement learning, graph neural network, or generative modeling approaches tailored to EDA applications - Collaborate on integrating ML components into existing EDA pipelines and design flows - Document methodologies, model assumptions, and technical approaches clearly and reproducibly

Required qualifications - Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., DAC, ICCAD, NeurIPS, ICML, or equivalent) - Master's or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related quantitative field - Demonstrated expertise in both machine learning and chip design or EDA (e.g., VLSI, RTL design, physical design, or verification) - Strong problem-solving skills and ability to work independently on technical and research tasks

Preferred qualifications - Experience with EDA tools and frameworks (e.g., Cadence, Synopsys, OpenROAD, or similar) - Familiarity with ML-for-EDA research (e.g., ChipNeMo, AlphaChip, or graph-based placement methods) - Background in TA'ing or teaching VLSI design, computer architecture, or machine learning courses

Why apply - Flexible Time Commitment – Work on your schedule while tackling meaningful engineering 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 applying ML to frontier chip design problems - Professional Growth – Sharpen your skills on complex, real-world EDA datasets and design flows

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