Engineering Manager, Data & Machine Learning Systems

Apple • Cupertino, CA

9h

About The Position

Imagine shaping how millions of people discover content they love on the App
Store, Apple Music, and Apple TV+. Our team is responsible for the intelligence
that powers these deeply personal experiences. We are at a pivotal moment,
defining the next generation of personalization. We build the foundational
capabilities that empower product and research teams to deliver
hyper-personalized experiences while maintaining an uncompromising commitment to
user privacy. We believe that deep personalization shouldn't require
compromising user trust, and we are pioneering the decentralized data systems to
prove it.

DESCRIPTION

We are looking for a visionary Engineering Manager to lead a team of pioneering
ML engineers. Your team will build the systems that securely process, combine,
and deliver the critical user and content signals needed for personalization,
spanning from edge devices to cloud backends. You will guide the development of
high-performance stacks that transform raw data into governed, discoverable
intelligence. As a leader, you will navigate the complex intersection of
petabyte-scale data engineering, machine learning systems, and strict privacy
compliance, ensuring your team delivers the foundational data layer that powers
the next generation of intelligent experiences.

Requirements

• BS or MS in Computer Science, Engineering, or a related field.
• Leadership Experience: Proven track record of managing and scaling engineering teams focused on data platforms, machine learning systems, or large-scale backend stacks.
• Technical Foundation: Deep architectural understanding of distributed data processing (e.g., Spark, Flink), high-throughput backend engineering (e.g., Java, Go), and ML training environments (Python).
• Delivery & Execution: Demonstrated ability to lead complex, cross-functional projects from conception to production at massive scale.
• Strategic Thinking: Experience defining technical roadmaps, navigating ambiguity, and balancing short-term product needs with long-term architectural health.

Nice To Haves

• Hybrid/Edge Computing: Experience leading teams that build systems bridging cloud stacks with on-device or edge compute environments.
• ML Ecosystem Knowledge: Familiarity with the lifecycle of machine learning models, feature stores, vector search, and dense embeddings.
• Data Governance: Experience implementing semantic layers, data catalogs, and automated compliance systems in heavily regulated environments.
• Privacy-Preserving Tech: Passion for privacy and an understanding of Privacy-Enhancing Technologies (PETs), secure enclaves, or decentralized data strategies.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Number of Employees: 5,001-10,000 employees