Apple
Engineering Manager — Foundations, Finance Data & Technology
$238K–$356K
Cupertino, California · Manager · Full-time · Posted Oct 12, 2026
- Experience
- 10+ years of software engineering; 3+ years leading managers and senior technical leaders across multiple teams. Preferred: 12+ years of software engineering and 6+ years leading managers and senior technical leaders across multiple teams.
- Education
- Bachelor’s degree in Computer Science, Statistics, or Engineering, or equivalent practical experience. Preferred: MS or PhD in these fields, or equivalent practical experience.
About the job
Leads the Finance Foundations engineering organization, building shared cloud, data, application, developer, and AI platforms for finance teams.
What you'll do
- Set strategy and oversee architecture and execution for reusable finance platforms, including production-grade agentic AI and GenAI capabilities.
- Lead and develop engineering managers, architects, and technical leads; establish engineering standards, governance, and platform health metrics.
- Coordinate platform priorities and shared standards with IS&T, finance, security, privacy, legal, and delivery teams.
What they ask for
- 10+ years of software engineering experience.
- 3+ years leading managers and senior technical leaders across multiple teams.
- Bachelor’s degree in Computer Science, Statistics, or Engineering, or equivalent practical experience.
Nice to have
- Hands-on experience designing and shipping an agentic AI solution into production; finance or regulated-industry experience is strongly preferred.
- Experience with finance or enterprise platforms, large-scale platform engineering, cloud-native architectures, distributed systems, and modern data platforms.
Pay
$237,600 and $356,400 (base pay range; USD)
Tools
cloud infrastructure, data platforms, application frameworks, developer tooling, AI/ML services, GenAI, RAG/semantic retrieval, knowledge graphs
AI focus
Build production-grade agentic AI and GenAI as reusable enterprise capabilities, including model lifecycle management, evaluation, observability, and responsible AI governance.
Model-written summary · the original posting is authoritative