Principal Quality Engineer (CAE Cross-Domain)

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology. Position Summary We are seeking a Principal QA Engineer with strong CAE simulation software experience, deep quality engineering expertise, hands-on Python automation skills, and practical understanding of modern AI-assisted engineering practices. This role is responsible for defining and executing effective test strategies for Apex and related CAE products, validating complex engineering workflows, improving automation coverage, and driving high-quality release outcomes. The successful candidate will act as a senior quality authority who can work closely with development, product management, QA, application engineering, and customer-facing teams to identify risks early, strengthen validation practices, investigate complex issues, and ensure that CAE workflows are reliable, usable, performant, and technically accurate. Key Responsibilities • Lead, coach, and develop a team of QA engineers, establishing clear priorities, ownership, and expectations. • Provide day-to-day technical guidance while encouraging independent problem solving, collaboration, and continuous learning. • Own the team’s test strategy, release qualification approach, automation roadmap, and quality improvement plans. • Plan and balance testing activities across new features, regression, integration, performance, scalability, and end-to-end customer workflows. • Partner with development and product management to clarify requirements, identify quality risks early, and define appropriate validation coverage. • Review test plans, automation designs, engineering models, defect reports, and release-readiness evidence. • Remain hands-on in complex technical investigations, high-risk validation, customer-reported issues, and critical release decisions. • Drive the creation and maintenance of robust Python-based test automation and sustainable automation frameworks. • Use quality metrics and engineering evidence to communicate product status, risks, dependencies, and recommended actions to stakeholders. • Improve QA processes, test effectiveness, traceability, execution efficiency, and cross-team consistency. • Promote Agile collaboration through effective planning, reviews, retrospectives, and follow-through on improvement actions. • Coordinate effectively with global teams across time zones and support a respectful, inclusive, and accountable team culture. AI Leadership and Modern QA Practices • Guide the practical and responsible adoption of AI-assisted tools for test design, code generation, defect analysis, documentation, and quality reporting. • Ensure AI-generated code, test cases, summaries, and engineering outputs are reviewed, verified, traceable, and appropriate for their intended use. • Identify use cases where AI can improve test coverage, productivity, defect discovery, and knowledge sharing without weakening engineering judgment. • Help the team understand the capabilities, limitations, and risks of Generative AI and Large Language Models. • Protect confidential information and reinforce organizational policies when using external or enterprise AI tools. • Encourage experimentation with measurable outcomes and share effective AI-enabled QA practices across teams. Required Qualifications Education • Bachelor’s degree in Mechanical Engineering, Aerospace Engineering, Automotive Engineering, Computer Science, or a related discipline. • Master’s degree preferred. Experience • 12+ years of experience in software quality assurance, CAE simulation software validation, test strategy, release qualification, automation, or related engineering software domains. • Strong experience in software quality assurance, test strategy, release qualification, and defect management. • Proven ability to define detailed test plans, validation workflows, acceptance criteria, defect analysis approaches, and