7 jobs for "Senior Python Software Engineer (Quantitative Risk)"

Senior Software Engineer - Network Automation Architect

At Equinix - Remote / Flexible

Posted on Sep 10

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Principal Quality Engineer (CAE Cross-Domain)

At Cadence Design Systems - Remote / Flexible

Posted on Oct 16

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Senior Principal Engineer, Enterprise Networking Architecture, Automation and AI

At Equinix - Remote / Flexible

Posted on Sep 16

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Senior Principal Engineer, Enterprise Networking Architecture, Automation and AI

At Equinix - Remote / Flexible

Posted on Sep 16

View job Apply on company site

Principal Quality Engineer (CAE Cross-Domain)

At Cadence Design Systems - Remote / Flexible

Posted on Oct 16

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Senior Principal Engineer, Enterprise Networking Architecture, Automation and AI

At Equinix - Remote / Flexible

Posted on Sep 16

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Product Engineer II - Web Services

At Esri - Remote / Flexible

Posted on Sep 23

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Cadence Design Systems

Principal Quality Engineer (CAE Cross-Domain)

Remote / FlexiblePosted 22 days ago

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At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

Position SummaryWe 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 QualificationsEducation• 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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About Cadence Design Systems

Cadence is a pivotal leader in computational software.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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