Nvidia Senior VLSI CAD AI Automation Engineer 

Israel, North District 

143492395

What you'll be doing:

• You will be responsible for developing and integrating advanced CAD solutions and automation flows using AI and machine learning for VLSI design, verification, and implementation.
• Work closely with design, verification, and CAD teams to identify areas for improving VLSI workflows using advanced tools and methods.
• Research, prototype, and deploy AI-based algorithms.
• Develop and maintain scripts and automation infrastructure to enable seamless adoption of AI tools in the VLSI design process.
• Continuously review emerging AI technologies and methodologies to keep our CAD environment up-to-date.
• Provide technical support and training to engineering teams on AI-enabled CAD flows and best practices.

What we need to see:

• B.Sc./M.Sc. in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience.
• 5+ years of experience in VLSI CAD tool development, with a strong focus on integrating AI/ML techniques into EDA workflows.
• Proficiency in Python and at least one AI/ML framework (such as TensorFlow, PyTorch, or scikit-learn).
• Hands-on experience with VLSI physical design and familiarity with industry-standard EDA tools (e.g., Synopsys, Cadence).
• Knowledge of data preprocessing, feature engineering, and model deployment as applied to VLSI design challenges.
• Experience developing and maintaining automation scripts (Python, Perl, Tcl, Make).
• Strong analytical skills in evaluating the impact of AI solutions on design quality, performance, and productivity.
• Excellent communication skills and the ability to work cross-functionally in a fast-paced environment.
• Self-motivation, attention to detail, and a track record of delivering robust solutions to production.

Ways to stand out from the crowd:

• Demonstrated experience deploying AI/ML models in production VLSI CAD environments.
• Contributions to open-source AI/EDA projects or publications in relevantconferences/journals.
• Deep understanding of VLSI design challenges-such as timing closure, power optimization, or yield enhancement-and how AI can address them.
• Experience with cloud-based or distributed compute environments for large-scale AI training and inference.
• Strong ownership, curiosity, and a passion for continuous learning and innovation.