Sr. Machine Learning Engineer - Finance

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 and global 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 require you to be collaborative by learning intra-team and business process in order to build infrastructure and services to enable an effective Machine Learning practice. You will help lead the charge by developing a strong ML Ops process in a dynamic Finance environment where you will deal with unique challenges specific to Finance organizations, such as SOX and regulatory compliance. Your ability to instill and proliferate strong software engineering practices into team data science and machine learning processes will be critical.

Preferred Qualifications

Graduate degree (computer science, data science, math, quantitative finance, or similar discipline) with five years experience
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
Experience with front end (.js experience)

Minimum Qualifications

Undergraduate degree (computer science, data science, finance, economics, accounting, or related business discipline) with seven years demonstrated experience
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 DRY principles, modularity, and testing standards, with version control, code review.
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 cloud platforms (AWS, GCP, Azure)
Ability to explain technical details to non-technical audiences
Understands and advocates version control, test driven development and strong CI/CD process