Finance Digital Transformation - Senior Machine Learning Engineer

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 senior machine learning engineer in Finance, you’ll play an integral and global role in building the platform, data foundations, and services used for transforming Finance’s organization.

Description

You’ll learn intra-team and business process to build infrastructure and services enabling an effective Machine Learning practice. You will help lead the charge by developing robust AIML-driven processes and extending scalable platforms to optimize financial operations in a dynamic environment. You will tackle unique challenges specific to Finance organizations — including SOX compliance, regulatory requirements, cost variance analysis, margin analysis, and scenario modeling — while driving automation and efficiency across end-to-end finance workflows. Your ability to instill and proliferate strong software engineering practices into team data science and machine learning processes will be critical.

Preferred Qualifications

Experience inheriting and modernizing legacy ML infrastructure without disrupting existing users
LLMOps familiarity — evaluation pipelines, RAG infrastructure, prompt versioning, and production guardrails
Background in corporate finance, accounting, or supply chain; understanding of SOx, P&L, and close processes
Front-end experience for extending internal tooling and platform UIs a plus

Minimum Qualifications

Bachelors degree (CS, data science, engineering, or similar) with 7+ years experience
Demonstrated experience improving and extending existing AIML platforms and services
Hands-on ML platform experience: feature stores, registries, experiment tracking, and model serving
Strong debugging and operational instincts
Values engineering standards; modularity, testing, version control, and code review
CI/CD and MLOps experience strengthening existing pipelines; familiar with GitOps
Production Kubernetes and cloud platform experience
Working knowledge of ML algorithms; experience shipping generative AI and agentic solutions