About this role
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
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12+ years of experience in software quality assurance, CAE simulation software validation, test strategy, release qualification, automation, or related engineering software domains.
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Strong experience in software quality assurance, test strategy, release qualification, and defect management.
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Proven ability to define detailed test plans, validation workflows, acceptance criteria, defect analysis approaches, and release-readiness evidence for complex engineering applications.
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Demonstrated ability to provide technical leadership, coaching, feedback, and team-development support while promoting accountability and collaboration.
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Strong engineering judgment, structured problem-solving skills, and ability to make evidence-based quality recommendations in complex technical situations.
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Clear verbal and written English communication skills, with experience creating clarity across global teams and supporting changing priorities.
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Customer-focused mindset, curiosity, humility, and willingness to learn and apply emerging technologies responsibly.
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Technical Skills
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Strong understanding of FEA theory, structural mechanics, strength of materials, engineering simulation, and engineering design concepts.
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Hands-on Python experience and a solid understanding of test automation architecture and maintainability.
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Experience with one or more commercial CAE tools such as MSC Apex, MSC Patran, MSC Nastran, HyperMesh, ANSYS, or ANSA.
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Experience validating engineering software, CAE applications, numerical workflows, or complex desktop products is highly desirable.
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Understanding functional, regression, integration, performance, scalability, and end-to-end testing.
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Familiarity with Agile software development and continuous improvement practices.
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Preferred AI Experience
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Practical familiarity with AI-assisted tools such as GitHub Copilot, Microsoft Copilot, ChatGPT, or comparable technologies.
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Understanding Generative AI and Large Language Model concepts, including accuracy, consistency, privacy, security, and validation considerations.
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Experience introducing AI-assisted development or testing practices within an engineering team is a plus.
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Ability to distinguish between tasks suitable for AI assistance and tasks requiring direct domain expertise or formal verification.
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