Tech Lead Machine Learning Engineer - Finance

Weekly Hours: 40

Role Number: 200645666-0836

Summary

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and curiosity to your job and there's no telling what you could accomplish. Do you love thinking analytically? Just as our customers find value in Apple products, the Finance group finds value for both Apple and its shareholders. As a machine learning engineer in Finance, you’ll play an integral role in building the data foundations, services, and platforms used for delivering insights and automating decisions for Apple’s Finance organization.

Description

This role will be the technical lead for product cost, supporting our Operations Finance organization. You will work as part of a multi-discipline engineering pod with data and software engineers, product managers and program managers. Your ability to learn business processes and instill strong engineering practices into team machine learning processes will be critical. A key part of your role will be to operationalize AI solutions, bridging the gap between prototype and production to rapidly and reliably deliver value to the Finance organization.

Minimum Qualifications

• At least 8 years experience in an engineering role

• At least one year experience effectively leading engineers and collaborating cross-functionally, translating technical concepts for diverse audiences and converting ideas into solutions with strong process and data understanding

• Experience building data models and scalable pipelines using SQL and big data technologies, with expertise in data ops best practices

• Experience developing in Python while following and advocating for DRY principles, modularity, testing standards, version control, and code reviews. Experience with front end (.js experience)

• Experience applying ML algorithms for regression, classification, and anomaly detection; build generative AI and agentic solutions; implement MLOps/LLMOps including CI/CD, drift monitoring, and familiarity working with cloud platforms (AWS, GCP, Azure)

• Graduate degree (computer science, data science, math, quantitative finance, or similar discipline)

• Undergraduate degree (computer science, data science, finance, economics, accounting, or related business discipline)

Preferred Qualifications

• Previous experience working in a corporate finance, accounting, or supply chain organization

• Understanding of or ability to learn financial statements, P&L impact, high level accounting principles, SOX and tax compliance and month-end close process