The role involves working with the Intelligent Quality Systems team to build ML-powered systems that enhance software quality at scale. The team focuses on various aspects of the testing lifecycle, including test selection, failure triaging, and data platform development. Candidates will apply machine learning techniques to real-world software systems, addressing challenges like change-impact prediction and automated test recommendation. This position requires a strong blend of ML expertise and software engineering skills to bridge research and production realities.

• 3+ years of experience with machine learning in academic or professional settings, with demonstrated work on substantial ML projects beyond coursework.
• Strong programming and software engineering skills with the ability to write clean, maintainable, production-quality code.
• Practical understanding of ML fundamentals, model training, evaluation, and debugging, and the ability to implement algorithms from papers or specifications.
• BS or MS in Computer Science, Machine Learning, Statistics, or a related field.
• Experience with NLP, large language models, code understanding, or retrieval-augmented generation (RAG).
• Background in computer vision or multimodal ML.
• Experience building or maintaining data pipelines and ML infrastructure at scale.
• Familiarity with software testing, CI/CD systems, or developer tooling.
• Experience working with large-scale structured or semi-structured data such as logs, test results, or code diffs.
• Comfort operating in environments where ground truth labels are noisy, ambiguous, or expensive to obtain.

• Design and build ML-powered systems that transform Apple's approach to software quality.
• Intelligently select relevant tests for code changes and automatically triage test failures to their root cause.
• Validate UI and audio experiences using vision models and identify test coverage gaps.
• Prototype and evaluate techniques for change-impact prediction and LLM-powered failure analysis.
• Design data collection and evaluation strategies while working with large-scale test result and code change data.
• Build robust systems that operate reliably across a large software engineering organization.
• Identify gaps in data quality, modeling assumptions, and system design before they become issues at scale.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.