Job Summary

Job Summary 

• Responsible for quality assurance of PDKs, ensuring accuracy, consistency, and compliance across design and verification flows 

• Enables silicon-ready releases through rigorous QA validation, automation, and governance 

Key Responsibilities 

• Execute and drive QA validation across PDK components and flows 

• Perform verification, regression, and issue analysis across nodes 

• Ensure compliance with QA standards, documentation, and quality gates 

• Lead cross-domain issue resolution with design, CAD, and customers 

• Build and scale automation frameworks for QA, regression, and validation 

• Drive AI adoption for failure analysis, pattern detection, and regression optimization 

• Manage end-to-end QA delivery, including planning, tracking, and escalations 

• Establish governance frameworks: reviews, audits, documentation standards 

• Mentor teams and improve QA competency and best practices 

• Drive multi-project QA execution and cross-team alignment 

• Define organization-wide QA strategy, roadmap, and transformation initiatives 

Key Responsibilities

• Strong understanding of PDK QA flows, physical verification, and design validation 

• Expertise in DRC/LVS/verification methodologies 

• Proficiency in EDA tools (Cadence/Synopsys/Verification tools) 

• Strong scripting knowledge: Python, SKILL, Shell 

• Capability to design automation frameworks and scalable QA systems 

• Strong analytical, debugging, and problem-solving skills 

• Ability to lead teams, drive delivery, and manage customer interactions 

Skill Requirements

• Cadence / Synopsys Custom Tools 

• Calibre, ICV, Pegasus, PVS 

• Python, SKILL, Shell, Tcl, Ruby 

• Automation & AI-driven QA frameworks 

• Version control and CI/CD pipelines 

Qualifications 

• B.E./B.Tech/M.E./M.Tech in Electronics / Electrical / VLSI or related field 

• 12 to 15 yrs experience in PDK QA, CAD, physical verification, or design automation 

Other Requirements

Nice to Have 

• Experience in multi-node QA environments 

• Automation framework development 

• Customer-facing QA delivery experience 

• AI/ML usage in QA workflows