Senior QA Engineer

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. 

Customer Escalation and Collaboration 

• 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. 

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.