Finance Digital Transformation - Senior Machine Learning Engineer
Apple
Accounting & Finance, Software Engineering, Data Science
Cupertino, CA, USA
USD 181,100-272,100 / year + Equity
Posted on May 15, 2026
magine 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.
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 strong AIML processes and extending platforms in a dynamic 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.
- Extend and improve existing platform capabilities for generative AI, agentic workflows, and machine learning inference use cases
- Drive SLO definition, close observability gaps, and strengthen operational posture across ML stack
- Develop and deploy frameworks for agentic AI, including evaluation and knowledge management
- Harden CI/CD and MLOps practices with testing, drift monitoring, and deployment guardrails
- Ensure controls are robust, auditable, and scalable within Finance governance frameworks
- Collaborate with delivery engineering pods to identify pain points and prioritize improvements
- 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
- 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