Quality Engineer

Summary

Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless.Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here. Employee Experience and Productivity (EEP) is part of IS&T and delivers some of the most widely used communication and productivity technology and services across Apple — from tools that enable collaboration to the support teams that keep Apple’s workforce running. EEP also leads user experience design and research, and manages the systems behind Apple’s original content studios. Within this team is Employee Engagement & Engineering, a small, global team that quickly delivers with long-term vision, building durable, reliable systems meant to last. Our focus is on crafting high-quality, user-centred experiences — delivering AI and system interoperability to make every interaction intuitive, seamless, and genuinely useful for the people who rely on our tools.

Minimum Qualifications

Experience in software engineering, quality engineering, or application operations as part of a team and understanding of software development lifecycle and agile/sprint-based delivery processes Experience testing AI applications evaluating model outputs, basic prompt engineering, and automating data validation. Experience with testing methodologies — manual testing, UAT, regression, and exploratory testing B.S. or M.S. in Computer Science, Software Engineering, or equivalent experience

Preferred Qualifications

Proficiency in testing and operations within Apple platforms, including macOS and iOS. Expertise in CI/CD pipelines and release management processes. Experience with test automation frameworks such as XCTest, XCUITest, Selenium, and Playwright. Experience in AI prompt engineering. Exposure to native application development in Swift or SwiftUI. Experience in customer-facing or support roles that necessitated escalation management. Familiarity with Jira, Radar, or analogous issue tracking systems for sprint and release management.

Description

As an Engineering Enablement engineer on the EEE team, you will take a holistic approach to supporting the quality and reliability of the applications we deliver — spanning quality assurance, troubleshooting, product understanding, and serving as the escalation point between users and the engineering team. You are the bridge between the people who use our applications and the engineers who build them. Working under the guidance of senior engineers, you will develop a deep understanding of how our products work and how people use them. You will drive quality engineering through testing and validation, respond to production issues as the first line of triage, ensure business requirements are accurately captured for development, and help the team ship confidently by managing processes that keep delivery smooth and predictable.

Responsibilities

Drive quality engineering through internal testing, including User Acceptance Testing (UAT) Design and generate realistic synthetic datasets, including adversarial and edge-case scenarios, that closely resemble real-world user interactions, prompts, and response patterns to support comprehensive project testing. Systematically review AI-generated outputs against a defined evaluation rubric to identify fabricated facts, invented references, non-existent entities, and unsupported claims. Analyse evaluation data to identify recurring failure patterns, quality degradation trends, and areas of improvement across model versions and product domains. Generate actionable insights from validation results to prioritize model retraining efforts and prompt refinement. Design and build end-to-end automated evaluation pipelines that streamline the validation workflow, from input ingestion and response evaluation to scoring and reporting. Integrate LLM-as-a-judge frameworks and custom evaluation models into the automation pipeline to enable consistent, repeatable, and scalable quality assessments as part of the CI/CD pipeline. Implement alerting and monitoring mechanisms within the AI automation framework to detect quality drops in real time and trigger escalation workflows. Partner with ML engineers, prompt engineers, product teams, and content/legal teams to align evaluation standards with business goals and compliance requirements. Maintain documentation of technical processes, systems, and troubleshooting. Build relationships with internal business partners to understand their needs and advocate for their experience.