CAD Back-end Lead

Develop, integrate, qualify, release, and support CAD flows spanning Custom Design, physical implementation, and Physical Verification across multiple projects and advanced process technologies.

Own and develop reusable CAD frameworks for custom design and custom layout, as well as block-level, hierarchical, subsystem-level, and full-chip physical implementation.

Integrate EDA tools from Cadence, Synopsys, Siemens EDA, Ansys, and other vendors into consistent, version-controlled, and reproducible design environments.

Partner with domain experts to translate Custom Design, Characterization, Physical Design, STA, Power Integrity, and Physical Verification methodologies into automated production flows.

Integrate and qualify foundry PDKs, technology files, standard-cell libraries, memories, IP collateral, RC corners, extraction decks, signoff rule decks, and EDA tool configuration files.

Own back-end flow releases, including configuration management, regression testing, qualification, documentation, deployment, versioning, and rollback.

Establish source-control and release-management practices using Git, GitLab, Perforce, or equivalent platforms, including branching, tagging, code reviews, release baselines, and change tracking.

Build regression infrastructure to validate flow functionality, EDA tool upgrades, PDK updates, methodology changes, and design collateral before production deployment.

Develop automated quality checks and dashboards covering flow status, runtime, resource usage, failures, design quality, and key QoR metrics.

Work with central CAD and Infrastructure teams on compute requirements, job scheduling, storage, EDA tool deployment, license usage, and capacity planning for Custom Design and back-end workloads.

Partner with foundries, IP providers, and EDA vendors to resolve issues involving EDA tools, PDKs, technology files, rule decks, and design methodologies.

Evaluate new EDA technologies based on measurable improvements in quality of results, turnaround time, scalability, reliability, engineering productivity, and cost.

Actively use AI-assisted coding and engineering tools for software development, test generation, code review, documentation, log analysis, debugging, and flow optimization.

Architect and develop agentic AI workflows for EDA flow orchestration, regression monitoring, failure classification, root-cause analysis, QoR comparison, knowledge retrieval, and production support.

Integrate AI agents with EDA tools, job schedulers, version-control systems, regression databases, dashboards, and internal engineering knowledge repositories.

Establish appropriate security, access-control, IP-protection, validation, and human-approval mechanisms for applying AI to proprietary design data and production EDA environments.

Define measurable success criteria for AI-assisted workflows and transition successful prototypes into qualified production use.

Establish coding standards, code-review practices, validation requirements, documentation standards, and release processes for back-end CAD development.

Mentor CAD engineers and promote strong practices in software development, automation, version control, regression testing, agentic AI development, and production flow support.