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Apple • Seattle, Washington, United States
Role & seniority: Senior Quality Engineer / senior technical leader responsible for quality across Apple's ML platform and AI infrastructure.
Stack/tools: Java, Python; test frameworks (PyTest, JUnit); CI/CD; microservices/cloud architectures; performance testing; ML data pipelines and model training workflows; Docker, Kubernetes; cloud platforms (AWS/GCP/Azure).
Lead end-to-end quality initiatives for ML pipelines, data workflows, deployment pipelines, and MLOps tooling.
Define and implement test frameworks tailored for ML systems; influence architecture and testing strategies.
Drive multi-functional quality programs, guide teams on scalable, reliable AI infrastructure, and ensure quality at scale across data ingestion, training, and deployment.
10+ years in software development and/or test automation; 3+ years leading complex, distributed system testing.
Proficiency in Java or Python; ability to write reusable test frameworks.
Experience with test design, CI/CD, test automation at scale; performance testing of large-scale systems.
Experience with PyTest/JUnit (or equivalent); ability to lead testing for backend/platform systems (microservices/cloud).
Experience testing AI/ML systems or platforms with ML training or data pipelines; understanding of ML lifecycle; GenAI experience.
Cloud experience (AWS/GCP/Azure) and containerized environments (Docker, Kubernetes).
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Imagine what we could do together. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there’s no telling what we could accomplish! The Apple Services Engineering team is one of the most exciting examples of Apple’s long-held passion for combining art and technology. We are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, Apple Books, Apple Sports and Apple Fitness. And we do it on a massive scale, meeting Apple’s high standard for quality and excellence, to deliver a huge variety of entertainment in over 50+ languages to more than 150 countries. If you are looking for an opportunity to grow in a technical capacity by leveraging your skills along with building up solid domain knowledge and automated testing strategies and systems around Apple's services offerings in the AI/ML space, we would love to talk to you!
DESCRIPTION
We are seeking an experienced engineer with a passion for quality engineering and a working understanding of AI/ML systems to lead quality efforts across our machine learning platform. In this strategic role, you will shape how we test and validate machine learning pipelines, data workflows, and platform services that power our AI products at scale. You will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. As a senior technical leader, you will influence architecture, establish test frameworks tailored for ML systems, and guide teams on scalable, testable, and reliable AI infrastructure.
MINIMUM QUALIFICATIONS
10+ years in software development and/or test automation, with at least 3 years leading complex, distributed system testing. B.S. in Computer Science or similar field, M.S. preferred. Strong programming experience in Java or Python, with ability to write reusable test frameworks. Proven ability to lead testing efforts for large-scale, backend or platform systems (ideally including microservices or cloud-based architectures). Deep understanding of test design methodologies, CI/CD practices, and test automation at scale. Experience with test frameworks and tools such as PyTest, JUnit, or equivalent. Experience with performance testing of large scale systems. Skilled in driving multi-functional quality programs and influencing engineering architecture and tooling.
PREFERRED QUALIFICATIONS
Experience testing AI/ML systems or platforms that include ML model training or data pipelines. A general understanding of Machine Learning Lifecycle. Have worked on projects using GenAI. Experience working with cloud platforms (AWS/GCP/Azure) and containerized environments (Docker, Kubernetes). Contributions to quality strategies in AI/ML product teams or research settings.