Quality Assurance Automation Engineer - Manufacturing Systems and Infrastructure
Explicitly requires vibe coding skills and AI-assisted workflows; uses GitHub Copilot, Cursor, ChatGPT and other AI assistants to generate and maintain tests.
About the Role
Join Apple's Manufacturing Systems and Infrastructure team as a Quality Assurance Automation Engineer to design and maintain AI-driven test automation across web, mobile, and API interfaces. The role focuses on leveraging Large Language Models and AI-native testing tools to generate synthetic datasets, implement smart test selection in CI/CD, and drive GenAI adoption across QA.
Job Description
Role
As a Quality Assurance Automation Engineer on the Manufacturing Systems and Infrastructure (MSI) team, you will build and maintain AI-enabled automated test suites for web, mobile, and API systems that support large-scale manufacturing operations. You will design automation frameworks, generate synthetic datasets for edge-case and load testing, integrate intelligent execution strategies into CI/CD, and promote GenAI best practices across QA.
Key Responsibilities
- Use LLMs and AI coding assistants (e.g., GitHub Copilot, Cursor, ChatGPT) to generate, refactor, and document automated test scripts for web, mobile, and API interfaces.
- Design, build, and maintain scalable test automation frameworks and integrate AI-native testing tools (e.g., Applitools, Testim, Mabl).
- Generate large, diverse, and secure synthetic datasets for edge-case and load testing using generative AI techniques.
- Integrate automated suites into CI/CD pipelines (GitHub Actions, Jenkins) and implement ML/AI-driven smart execution strategies to determine optimal test selection per commit.
- Use AI tools to parse server logs, stack traces, and crash reports for root-cause identification and automated bug categorization.
- Partner with product and engineering teams to define test strategies, maximize coverage, and reduce maintenance via intelligent automation.
- Champion AI adoption in QA by creating prompt libraries, guidelines, and best practices for GenAI-driven test creation and acceptance criteria.
Minimum Qualifications / Requirements
- 5+ years of experience in QA Automation, System Development in Test (SDET), or a similar engineering role.
- Expertise in Python, C/C++, Objective-C, and Swift, and familiarity with AI-assisted development workflows.
- Deep experience applying AI techniques to generate, optimize, and maintain automated test suites with frameworks such as Playwright, Cypress, Selenium, Appium, or XCTest.
- Demonstrated ability to write effective prompts to extract test scenarios, edge cases, and automation code from LLMs.
- Familiarity with AI-powered testing tools (e.g., Applitools Eyes, ReportPortal’s ML auto-analyzer, self-healing UI tools).
- Solid understanding of CI/CD pipelines, Docker, and version control (Git).
- Ability and willingness to travel up to 30% (domestic and international).
- Bachelors or Masters in Computer Science or related field.
Preferred Qualifications
- Experience designing and generating synthetic datasets for large-scale load and edge-case testing.
- Hands-on experience implementing ML/AI-driven smart test-selection or “smart execution” strategies in CI/CD.
- Experience building and curating prompt libraries or GenAI usage guidelines for QA or engineering teams.
- Strong written and verbal communication, ownership mindset, and a focus on reducing flaky tests through resilient automation design.