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 release-readiness evidence for complex engineering applications.
• Demonstrated ability to provide technical leadership, coaching, feedback, and team-development support while promoting accountability and collaboration.
• Strong engineering judgment, structured problem-solving skills, and ability to make evidence-based quality recommendations in complex technical situations.
• Clear verbal and written English communication skills, with experience creating clarity across global teams and supporting changing priorities.
• Customer-focused mindset, curiosity, humility, and willingness to learn and apply emerging technologies responsibly.

Technical Skills

• Strong understanding of FEA theory, structural mechanics, strength of materials, engineering simulation, and engineering design concepts.
• Hands-on Python experience and a solid understanding of test automation architecture and maintainability.
• Experience with one or more commercial CAE tools such as MSC Apex, MSC Patran, MSC Nastran, HyperMesh, ANSYS, or ANSA.
• Experience validating engineering software, CAE applications, numerical workflows, or complex desktop products is highly desirable.
• Understanding functional, regression, integration, performance, scalability, and end-to-end testing.
• Familiarity with Agile software development and continuous improvement practices.

Preferred AI Experience

• Practical familiarity with AI-assisted tools such as GitHub Copilot, Microsoft Copilot, ChatGPT, or comparable technologies.
• Understanding Generative AI and Large Language Model concepts, including accuracy, consistency, privacy, security, and validation considerations.
• Experience introducing AI-assisted development or testing practices within an engineering team is a plus.
• Ability to distinguish between tasks suitable for AI assistance and tasks requiring direct domain expertise or formal verification.N/A

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