About the role
Position 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.
EOE/Minorities/Females/Vet/Disability
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