Principal Quality Engineer (CAE Cross-Domain)

What You'll Do

• 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 We’re doing work that matters.
• Help us solve what others can’t.