The Machine Learning Engineer, AI/ML Model Quality role focuses on ensuring the integrity of data that powers AI systems at scale, particularly within Wallet, Payments, and Commerce. The position involves building and maintaining intelligent systems, validation frameworks, and monitoring pipelines to uphold data quality. The engineer will collaborate with Data Engineering, Privacy, and Legal teams to define observability metrics and lead telemetry analysis. This role is critical in supporting the deployment of machine learning features that impact hundreds of millions of users.

• A Bachelor's degree with hands-on experience in ML/AI model quality or a M.S or Ph.D in a related quantitative field is strongly preferred.
• 3+ years of experience in defining and tracking ML model health metrics in production.
• 3+ years of experience in Machine Learning, data science, or a closely related analytical role.
• Proficiency in Python, common ML frameworks like PyTorch, and SQL for complex data analysis.
• Experience querying and analyzing large-scale datasets using distributed computing frameworks.
• Solid understanding of statistical methods including hypothesis testing and data drift detection.
• Experience with GenAI or LLM systems and their quality failure modes.
• Strong communication skills to translate complex data findings into actionable insights.
• Experience with data visualization and dashboarding tools.
• Familiarity with LLM evaluation frameworks or techniques.
• Experience with Bayesian or causal graph-based approaches to synthetic data generation.
• Familiarity with confidence calibration techniques and uncertainty quantification.
• Experience with ML monitoring or observability platforms.
• Experience working with privacy-constrained data or under regulatory compliance frameworks.
• Background in financial services, fintech, or consumer payment products.

• Build and maintain intelligent systems and validation frameworks.
• Ensure data quality for machine learning models across Wallet, Payments, and Commerce.
• Collaborate with cross-functional teams to define and analyze observability metrics.
• Lead telemetry analysis across GenAI workflows.
• Monitor the health of training and validation datasets.
• Translate raw quality signals into insights that drive decisions.
• Develop models to calibrate and measure robustness of data.
• Present complex ML telemetry using data visualization tools.

• 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.