Software Quality Engineer, Retail & Marcom Engineering

Join us, the team that serves as Apple’s nerve center: Apple Information Systems and Technology group! We are looking for Software Quality Engineers, who will work closely with other Software Engineers, Project & Product Managers, Product Designers, and other internal & external partners.

There are countless ways you can contribute here, from working on apple.com to software for digital & physical Apple Stores, from software for Apple Care to Apple-wide systems for identity, operations, recruiting, and people management, from software for Apple data centers to Apple-wide data processing & AI/ML systems.

When you join IS&T, you’ll help design and manage systems, frameworks, and apps that countless Apple customers and employees rely on every day. Together, we’ll explore all the ways to improve how Apple operates, freeing our employees to do what they do best, and crafting magical experiences for our customers.

Retail and Marcom Engineering, an IS&T team, builds and operates the systems and experiences that connect Apple's products with its customers. The team owns the technology behind both Apple's online and physical stores and drives the interactive marketing experiences and tools that keep creative operations moving. Together, those functions deliver the technology behind every product story Apple tells and every purchase a customer makes.

Apple’s IS&T team is looking for a Software Quality Engineer to contribute to design, analysis and certification of our applications. We are looking for someone who is equally passionate about quality and using lightweight processes to consistently achieve it.

The role would also involve interacting with multi-functional engineering and product teams to test features, perform root cause analysis, and drive the quality of customer’s experience.

Minimum Qualifications

4+ years experience in software quality assurance testing
Experience with modern test automation frameworks such as Playwright/Selenium, Appium with Java, Typescript/JS or Python
Build and improve QE frameworks, tools, and infrastructure that increase engineering productivity
Design and implement scalable automation across UI, API, integration, and end-to-end testing.
Experience in testing Web, API, iOS/iPad OS native applications

Preferred Qualifications

Own the quality strategy for one or more application areas.
Develop/Adopt GenAI-powered solutions for test case generation, test automation, defect analysis, test data generation, test execution, and quality intelligence.
Establish appropriate evaluation, reliability, security, and observability practices for GenAI-powered QE solutions.
Experience with performance, reliability, or production-readiness testing is highly desirable.
Experience in testing microservices and cloud-based applications
Evaluate and integrate AI/agentic testing tools into existing QE workflows.
Define test strategies for functional, integration, regression, performance, reliability, and production-readiness requirements.
Experience with CI/CD and integrating automated quality gates into engineering pipelines.
Understands log scanning to identify errors and root cause, with strong problem-solving, debugging, and analytical skills.
Hands on experience with Load, Resiliency & Performance testing and tools like Jmeter/Gatling.
Experience with Testing of GenAI based applications, Security and Accessibility testing is good to have
Experience in cloud technologies such as AWS/GCP and containerisation technologies such as Docker or Kubernetes is good to have
Partner with Dev engineers, EPMs, and Business teams to identify and mitigate quality risks early.
Strong communication and collaboration skills, with the ability to work effectively with Software Engineers and Product teams.
Hands-on experience leveraging AI agents, MCP servers, and LLM-driven developer tools to accelerate test automation and engineering workflows at scale.
Hands-on experience and working knowledge of building AI-related systems — such as MCP (Model Context Protocol) servers, AI agents, agentic workflows, or LLM-powered tools/integrations — for real engineering use cases