Engineering Program Manager - AI and Data Platforms

Summary

Do you love understanding every detail of how new technologies work? Join the team that serves as Apple’s nerve center, our Information Systems and Technology group. From Apple Pay to the Apple website to our data centers around the globe, you’ll help design and manage the massive systems that countless employees and customers rely on every day. You’ll join AiDP — the organization building Apple’s enterprise Generative AI and Data platforms. We don’t just manage AI programs; we run our own programs with AI. The EPM who joins us will ship AI platforms to tens of thousands of Apple employees, and will do it using the same agentic tooling they help deliver. AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning — including generative AI — along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple. This is a role for someone early in their program management career who is unusually fluent with AI tools and wants to define what the craft looks like next.

Minimum Qualifications

3+ years of industry experience in an engineering, technical product, or technical program role. 1+ years managing AI programs or projects across multiple teams toward a shared objective. Demonstrated use of AI tools in your own work — automating analysis, drafting, prototyping, or building something you then used. Be ready to show us what you built. BS in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Preferred Qualifications

Exposure to AI/ML or data platform programs — model deployment, evaluations, RAG or agentic systems, or data pipelines. Working understanding of how AI systems differ from deterministic software: non-determinism, evaluation, and what “quality” means when there’s no single correct output. Comfort with ambiguity in a fast-moving, high-visibility environment, exercised with discretion and integrity. Outstanding written and verbal communication; you can compress a complex program into three lines for an executive and expand it to a design review for engineers.

Description

Three things make this different from a traditional program role: AI systems don’t ship like software. Launch readiness isn’t a pass/fail test matrix. You’ll work with engineering to define evaluation criteria, track quality and regression signals as program health metrics, and make release calls under genuine uncertainty about infinite use cases. You’ll operate as an AI-Native EPM. We expect you to use AI agents and assistants as a working partner — drafting status, synthesizing meeting notes, building your own lightweight tooling, automating reporting that used to be manual. We measure EPMs on outcomes delivered, not hours of coordination performed. If you’ve never automated a piece of your own job, this isn’t the right fit. Adoption, Engagement, Retention is the deliverable. Shipping the platform is half the program. You’ll track usage, retention, and business impact, and feed what you learn back into the roadmap.

Responsibilities

Own end-to-end delivery for AI ad Data platform programs: scope, roadmap, dependencies, critical path, risks, and launch readiness. Partner with engineering to define evaluation and quality criteria for AI capabilities, and track them as first-class program health signals alongside schedule. Build and maintain a single source of truth on status — clear DRIs, sequenced dependencies, honest risk calls — and report to engineering and executive leadership with the right altitude and cadence. Drive consensus across teams with competing priorities; influence without authority across AiDP and partner organizations at Apple. Instrument adoption and business impact after launch, and use what you find to shape the next cycle of the roadmap. Use AI agents and assistants to automate your own program operations — reporting, triage, synthesis — and share what works with the broader PMO. Contribute to product definition by bringing a user-centric point of view and challenging decisions that don’t serve the user.