CAD Back-end Lead

Skills

About the Role

You will build, integrate, qualify, release, and support CAD flows across custom design, physical implementation, and physical verification. You will develop reusable frameworks, automate regression testing and quality monitoring, integrate EDA tools and foundry collateral, and develop agentic AI workflows for flow orchestration and production support.

Requirements

• Knowledge of the ASIC development lifecycle, custom design, physical implementation, signoff, physical verification, and tapeout flows
• Experience developing and supporting CAD flows across ASIC or SoC programs
• Experience integrating EDA tools from major vendors
• Knowledge of Cadence, Synopsys, Siemens EDA, and Ansys tools
• Experience integrating foundry PDKs, technology files, timing libraries, physical libraries, extraction decks, verification rule decks, and IP collateral
• Software development and scripting skills in Python, Tcl, Bash, Perl, or similar languages
• Experience with Git, GitLab, Perforce, or equivalent version-control platforms
• Experience with branching, tagging, merge requests, code reviews, release baselines, configuration management, and rollback
• Experience with automated regression testing, continuous integration, release management, and reproducible engineering practices
• Experience using AI coding assistants and engineering tools
• Experience developing AI- or LLM-based applications or agentic workflows
• Ability to validate AI-generated outputs before production deployment
• Knowledge of data security, access control, confidentiality, and IP protection
• Knowledge of Linux, distributed compute, batch scheduling, storage systems, and license-dependent EDA workloads
• Debugging and root-cause-analysis skills
• Experience supporting production design teams during major milestones and tapeout
• Communication and collaboration skills

Responsibilities

• Develop, integrate, qualify, release, and support CAD flows across custom design, physical implementation, and physical verification
• Develop reusable CAD frameworks for custom design, custom layout, and physical implementation
• Integrate EDA tools into version-controlled and reproducible design environments
• Translate design methodologies into automated production flows
• Integrate and qualify PDKs, technology files, libraries, IP collateral, extraction decks, and rule decks
• Manage back-end flow releases, regression testing, qualification, documentation, deployment, versioning, and rollback
• Establish source-control and release-management practices
• Build regression infrastructure for EDA tool upgrades, PDK updates, methodology changes, and design collateral
• Develop automated quality checks and dashboards for flow status, runtime, resource usage, failures, and QoR metrics
• Coordinate compute, scheduling, storage, tool deployment, license usage, and capacity planning
• Resolve EDA tool, PDK, technology-file, rule-deck, and methodology issues with vendors
• Evaluate EDA technologies based on QoR, turnaround time, scalability, reliability, productivity, and cost
• Use AI-assisted engineering tools for development, testing, review, documentation, debugging, and flow optimization
• Develop agentic AI workflows for EDA orchestration, regression monitoring, failure analysis, QoR comparison, and production support
• Integrate AI agents with EDA tools, schedulers, version-control systems, regression databases, dashboards, and knowledge repositories
• Establish security, access-control, IP-protection, validation, and human-approval mechanisms for AI workflows
• Define success criteria and transition qualified AI prototypes into production
• Establish coding, review, validation, documentation, and release standards
• Mentor CAD engineers