Engineering Manager, User u0026 Content Intelligence

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.

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 features 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.

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 feature stores, 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.

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 u0026 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.