Chip Design Machine Learning Expert

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to chip design and electronic design automation (EDA). You will complete tasks at the intersection of machine learning and VLSI design — including model development, optimization, and research tasks applied to placement, routing, synthesis, verification, and other EDA workflows. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge ML for hardware research, and a strong addition to your research portfolio.

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

Requirements

• 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