As a machine learning engineer in Finance at Apple, you will play a crucial role in building data foundations and platforms that deliver insights and automate decisions for the Finance organization. You will lead efforts in product cost and work within a multi-discipline engineering team. Your expertise in machine learning and data operations will be essential for operationalizing AI solutions. This position requires a strong understanding of business processes and the ability to bridge the gap between prototype and production.

• Graduate degree in computer science, data science, math, quantitative finance, or a similar discipline.
• Seven or more years of experience building data-driven solutions.
• Experience leading engineers and collaborating cross-functionally.
• Expertise in building data models and scalable pipelines using SQL and big data technologies.
• Proficiency in Python development while following DRY principles and testing standards.
• Experience applying machine learning algorithms for regression, classification, and anomaly detection.

• Lead the technical efforts for product cost within the Operations Finance organization.
• Collaborate with data and software engineers, product managers, and program managers.
• Operationalize AI solutions to deliver value to the Finance organization.
• Translate technical concepts for diverse audiences and convert ideas into actionable solutions.
• Implement MLOps/LLMOps including CI/CD and drift monitoring.

• 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.