DescriptionPosition Overview:Work closely with design teams and CAD/EDA stakeholders to identify workflow bottlenecks across the chip development lifecycle-such as DRC/CDC/STA debug, regression triage, PPA convergence, and ECO iteration. This role focuses on building scalable data pipelines and models, and integrating AI-driven solutions into production CAD and verification flows.
Responsibilities:- Identify and prioritize high-impact opportunities to apply AI/ML to chip design and verification workflows, spanning RTL-to-GDS, signoff, debug, and regression
- Build and maintain data pipelines to extract, normalize, and analyze signals from EDA tool logs, reports, run artifacts, and design metadata (e.g., timing reports, violations, coverage, failures)
- Develop ML models, heuristics, and analytics to improve efficiency and quality in areas such as STA, DRC/LVS, ECO optimization, and debug acceleration
- Integrate AI solutions into existing CAD infrastructure, including automation systems, regression frameworks, job schedulers, and design databases
- Collaborate with EDA vendors as needed for tool enablement, feature requests, debugging, and evaluation of vendor solutions versus internal implementations
Requirements:- BS or MS in Electrical Engineering, Computer Science, or equivalent industry experience
- 2+ years of experience in CAD/EDA flow engineering, design automation, or a related semiconductor workflow role
- Strong programming skills, with Python required; experience with shell, Tcl, or Perl as needed
- Hands-on experience applying machine learning to real-world engineering problems
- Solid understanding of at least one major EDA workflow domain (e.g., place & route, physical layout, DRC/LVS, STA, power, CDC, DFT, simulation/regressions)
- Experience working with large-scale, noisy operational data (EDA logs and reports) and building robust automation around it
Annual base salary for this role in California, US is expected to be between $110,000 - $145,000. Actual pay will be determined by several factors such as relevant skills and experience, and the pay of employees in a similar role.
