Senior QA Engineer
Summary
Senior QA engineer safeguarding the quality and release readiness of Onto Innovation's semiconductor metrology software: builds test strategies and automated regression suites wired into Azure DevOps CI/CD pipelines, uses AI tools like Copilot, and supports customer escalations. Requires 5+ years in QA/test automation and a language such as Python or C#.
Job Summary:
The Senior Software Quality Engineer will help ensure the quality, reliability, and release readiness of Onto Innovation's MBU software product, and related semiconductor metrology software platforms. The role combines end-to-end system validation, test automation, AI-assisted quality engineering, customer escalation support, and cross-functional collaboration.
Responsibilities:
Software Quality Assurance
· Develop and execute comprehensive test strategies, test plans, and test cases based on product requirements, system specifications, and technical designs.
· Perform functional, integration, system, interface, regression, performance, benchmarking, and end-to-end workflow testing.
· Validate workflows spanning equipment operation, measurement data acquisition, recipe setup, analysis, and solution output.
· Participate in release planning, quality risk assessment, defect triage, and release readiness reviews.
· Track and communicate quality metrics, defect trends, test coverage, and release risks.
- Test Automation and DevOps
· Design, develop, maintain, and improve scalable automated test frameworks and regression suites.
· Automate high-value functional, integration, regression, API, workflow, and performance tests.
· Integrate automated tests with Azure DevOps (ADO) pipelines and CI/CD workflows.
· Use Git and GitHub for source control, code review, collaboration, and automation asset management.
· Improve automation coverage, test execution efficiency, repeatability, traceability, and release confidence.
- AI-Assisted Quality Engineering
- Use AI-assisted engineering tools such as GitHub Copilot, Microsoft Copilot, and approved large language model (LLM) technologies to improve engineering productivity.
- Apply AI-assisted approaches to test design, test case generation, test script development, defect investigation, log analysis, root-cause exploration, and technical documentation.
- Evaluate AI-generated outputs critically and ensure that automated test assets meet quality, security, privacy, and maintainability expectations.
- Identify practical opportunities to introduce AI-enabled quality engineering while maintaining appropriate human review and governance.
- Reproduce, investigate, document, and validate customer-reported software issues.
- Collaborate with Software Development, Product Management, Applications, Characterization, Customer Support, Field Service, and other stakeholders to resolve critical issues.
- Help translate customer impact into clear engineering priorities, validation plans, and release recommendations.
- Contribute to root-cause analysis, corrective actions, preventive actions, and continuous process improvement.
Customer Escalation and Collaboration
Provide technical guidance and mentoring to other SQAengineers when needed.
Qualifications
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Software Engineering, Engineering, Physics, Chemistry, Material Science, or a related discipline.
- Five or more years of software quality assurance, software testing, test automation, or related engineering experience.
- Strong knowledge of software quality assurance methodologies, tools, processes, and defect management practices.
- Experience creating comprehensive test strategies, test plans, test cases, and release validation criteria.
- Hands-on experience designing or maintaining automated testing solutions.
- Proficiency in at least one programming or scripting language, such as Python, C#, Java, JavaScript/TypeScript, or PowerShell.
- Hands-on experience with Azure DevOps (ADO), Git/GitHub, CI/CD pipelines, work-item tracking, and test management practices.
- Strong analytical, troubleshooting, communication, and cross-functional collaboration skills.
- Experience working in Agile or Scrum software development environments.
Preferred Qualifications
- Practical experience using GitHub Copilot, Microsoft Copilot, or similar AI-assisted software engineering tools.
- Experience applying AI or machine learning techniques to software testing, defect analysis, quality analytics, or engineering productivity.
- Experience testing distributed systems, APIs, services, desktop applications, databases, hardware/software integrations, or complex end-to-end workflows.
- Experience supporting customer escalations and working with global customers, applications teams, field teams, or customer support organizations.
- Knowledge of semiconductor front-end processes, optical metrology, thin-film metrology, OCD metrology, numerical analysis, machine learning, or material characterization.
- ISTQB or another relevant software testing certification.