Machine Learning Engineer Intelligent Quality Systems

Our team of applied ML and software engineers builds intelligent systems that help Apples OS software ship faster and with higher quality. We apply state-of-the-art ML LLMs computer vision retrieval systems and large-scale data analysis throughout the software lifecycle. If youre excited by using ML to solve real large-scale problems in software quality we have challenging and meaningful work to do together.

The Intelligent Quality Systems team designs and builds ML-powered systems that transform how Apple approaches software quality at scale. We work across the testing lifecycle: intelligently selecting which tests are most relevant to a code change automatically triaging test failures to their root cause validating UI and audio experiences with vision models surfacing test coverage gaps from change descriptions and defect history and building the data platforms that tie it all this role youll bring ML ideas to life on real-world large-scale software systems. Youll prototype and evaluate techniques for problems like change-impact prediction LLM-powered failure analysis multimodal UI regression detection and automated test recommendation. Youll design data collection and evaluation strategies work with large-scale test result and code change data and build systems robust enough to operate reliably across one of the worlds largest software engineering organizations. You will be a crucial bridge between research ideas and production reality identifying gaps in data quality modeling assumptions and system design before they become issues at scale. Youll succeed here if you enjoy turning ideas into working ML systems care deeply about measurement and rigorous evaluation and find it rewarding to see your work directly improve the productivity and quality of software shipped to hundreds of millions of people.

Implement and refine ML solutions across the testing lifecycle test selection failure triage visual validation and test coverage analysisnRapidly prototype multiple algorithmic approaches to identify the most promising directionsnDesign and implement data collection labeling and evaluation strategies for training and measuring ML systemsnWork with large-scale test result and code change data to surface patterns and inform modeling decisionsnProactively identify research-to-production gaps and technical risks in proposed ML solutionsnCollaborate with applied scientists and software engineers to ship reliable high-impact ML systems

3 years of experience with machine learning in academic or professional settings with demonstrated work on substantial ML projects beyond courseworknStrong programming and software engineering skills; ability to write clean maintainable production-quality codenPractical understanding of ML fundamentals model training evaluation and debugging and ability to implement algorithms from papers or specificationsnBS 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)nBackground in computer vision or multimodal MLnExperience building or maintaining data pipelines and ML infrastructure at scalenFamiliarity with software testing CI/CD systems or developer toolingnExperience working with large-scale structured or semi-structured data (logs test results code diffs)nComfort operating in environments where ground truth labels are noisy ambiguous or expensive to obtain