Machine Learning Engineer, User & Content Intelligence

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Not verified last checked Jun 13, 2026

Salary

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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About this role

Machine Learning Engineer, User & Content Intelligence Seattle, United States of America 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. This is not a standard Data Engineering or ML role. We are looking for a pioneering engineer to join our team. You 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 engineer high-performance stacks that transform raw data into governed, discoverable intelligence, ensuring that machine learning models can seamlessly and securely access the right user and content features regardless of where that data physically resides. Architect Distributed Feature Access: Design and build the access layer that abstracts the physical location of data. Ensure that inference systems can seamlessly access real-time on-device context, cloud-based service history, and content metadata through a unified, familiar API. Engineer Large-Scale Feature Pipelines: Build robust, petabyte-scale pipelines that ingest and combine disparate data into coherent user profiles and rich content representations. Architect Training Data Systems: Transform raw

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