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 relevant conferences/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.
