Machine Learning Engineer - Intelligent Quality Systems

Apple • Cupertino, CA

4d

About The Position

The Intelligent Quality Systems team builds ML-powered systems to improve software quality across Apple’s software testing lifecycle. The team applies machine learning, large language models, computer vision, retrieval systems, and large-scale data analysis to help software ship faster and with higher quality. Systems include test selection based on code changes, test-failure triage, UI and audio validation, test-coverage gap discovery, and supporting data platforms.

In this role, you will prototype and evaluate methods for problems such as change-impact prediction, LLM-powered failure analysis, multimodal UI regression detection, and automated test recommendation. You will design data collection and evaluation strategies, work with large-scale test-result and code-change data, and help build reliable systems for use across Apple’s software engineering organization. The role bridges research and production by identifying issues in data quality, modeling assumptions, and system design before they become problems at scale.

Requirements

• At least 3 years of machine learning experience in academic or professional settings, with demonstrated work on substantial ML projects beyond coursework.
• Strong programming and software engineering skills, including 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.

Nice To Haves

• 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 working in environments where ground-truth labels are noisy, ambiguous, or expensive to obtain.

Responsibilities

• Implement and refine ML solutions across test selection, failure triage, visual validation, and test-coverage analysis.
• Rapidly prototype multiple algorithmic approaches to identify promising directions.
• Design and implement data collection, labeling, and evaluation strategies for training and measuring ML systems.
• Work with large-scale test-result and code-change data to surface patterns and inform modeling decisions.
• Proactively identify research-to-production gaps and technical risks in proposed ML solutions.
• Collaborate with applied scientists and software engineers to ship reliable, high-impact ML systems.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level