Software Development Engineer in Test,Retail and Marcom Engineering

Software Development Engineer in Test,Retail and Marcom Engineering

Sunnyvale, California, United States Software and Services

Marcom is the creatively-led global team that oversees Apple’s consumer facing marketing. We ensure the flawless development and execution of world‑class communications across all medias and platforms. The Marcom Quality Engineering team is seeking a Software Development Engineer in Test to lead test automation and quality initiatives for web applications and APIs. In this role, you will design scalable, intelligent automation frameworks, influence architectural decisions, and leverage AI/LLM‑powered tools for smarter test generation, faster failure analysis, and actionable quality insights—delivering reliable, high‑impact releases to millions of users worldwide.

This role sits at the intersection of test engineering, AI‑assisted quality tooling, and operational systems design. You will architect automation frameworks that scale across large, content‑rich applications, collaborate on LLM‑powered quality tools, and build the knowledge and accountability systems that keep Apple’s engineering teams in control of quality— even across a distributed, agency‑driven delivery model. From hands‑on test automation to vendor audits and data‑driven operational oversight, you will be the connective layer between technical quality and scalable program execution.

• Drive architectural decisions for test frameworks that scale across large, content‑rich applications, ensuring test results are trustworthy and actionable.
• Design, implement, and evolve test automation across web applications and services — including integration, functional, and E2E tests — ensuring reliability and long‑term maintainability across diverse program types.
• Partner with engineers building AI‑powered QE tools to apply LLM‑based approaches to test generation, failure analysis, and quality insights.
• Establish knowledge management standards for vendor partners, ensuring all artifacts, including onboarding materials, test strategies, etc. are systematically captured in queryable systems.
• Design and run scalable accountability systems for staff augmentation—tracking quality by team, standardizing agency handoffs, and enabling self‑serve onboarding—so performance and dependencies are driven by data, not escalation.
• Conduct audits of vendor deliverables (test cases, automation, defect reports) and drive continuous quality improvements

• Bachelor's degree in Computer Science, a related technical field, or 5 years of relevant industry experience in the US.
• Programming fundamentals demonstrated for large/shared test codebases, with the ability to ramp quickly as the stack evolves, including experience designing test architecture for asynchronous, event‑driven systems.
• Experience testing web applications using modern automation frameworks such as Playwright, WebdriverIO, or XCUITest, including practices for scalable, reliable, and maintainable test automation.
• Experience testing APIs, including RESTful and/or GraphQL services, with automated frameworks and an understanding of API design principles.
• Experience working with CI/test infrastructure, including improving reliability and feedback speed or operating CI runners and executors using tools like Git Hub Actions, Jenkins, or Harness.
• Experience working with vendor teams, including contributing to shared processes and onboarding standards.

• Familiarity with AI‑assisted quality techniques, such as using LLM‑enabled tools for test generation, failure analysis, triage, or supporting CI/CD quality gates.
• Experience improving testability of features by collaborating with software engineers and making deliberate choices around mocking, dependency management, and validating component and service interfaces.
• Experience with cross‑platform automation (web, native, APIs) and techniques to reduce test flakiness, improve time‑to‑signal, and increase result reliability.
• Familiarity with deterministic test data strategies, including seeding known records, masked production subsets, synthetic or golden datasets, and versioning.
• Expe…