What are the responsibilities and job description for the Machine Learning Engineer, User & Content Intelligence position at Apple, Inc.?
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.\\n\\nWe 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.
BS or MS in Computer Science, Data Engineering, Software Engineering, or a related field.\nSenior-Level Experience: A proven track record of shipping complex, large-scale data engineering, feature serving, or machine learning systems to production.\nMastery of Big Data & Serving: Expertise in designing distributed data processing systems using technologies like Spark and Flink, and building low-latency, high-throughput data serving layers or Feature Stores.\nStrong Software Engineering: Deep proficiency in Java or Go for building high-performance production backend systems, and Python for model training ecosystems.\nStrategic Data Mindset: Demonstrated experience thinking critically about data architecture, including data ontology, discoverability, and bridging distributed data sources.
Hybrid/Edge Computing: Experience building systems that bridge cloud backend systems with on-device or edge compute environments.\nEmbeddings & Vector Search: Familiarity with generating, managing, and serving dense embeddings for retrieval, ranking, and personalization systems.\nData Governance: Experience building feature stores, data catalogs, or implementing compliance-by-design in a regulated environment.\nPrivacy-Preserving Tech: Passion for privacy and an understanding of data minimization strategies, secure enclaves, or Privacy-Enhancing Technologies (PETs).
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.
BS or MS in Computer Science, Data Engineering, Software Engineering, or a related field.\nSenior-Level Experience: A proven track record of shipping complex, large-scale data engineering, feature serving, or machine learning systems to production.\nMastery of Big Data & Serving: Expertise in designing distributed data processing systems using technologies like Spark and Flink, and building low-latency, high-throughput data serving layers or Feature Stores.\nStrong Software Engineering: Deep proficiency in Java or Go for building high-performance production backend systems, and Python for model training ecosystems.\nStrategic Data Mindset: Demonstrated experience thinking critically about data architecture, including data ontology, discoverability, and bridging distributed data sources.
Hybrid/Edge Computing: Experience building systems that bridge cloud backend systems with on-device or edge compute environments.\nEmbeddings & Vector Search: Familiarity with generating, managing, and serving dense embeddings for retrieval, ranking, and personalization systems.\nData Governance: Experience building feature stores, data catalogs, or implementing compliance-by-design in a regulated environment.\nPrivacy-Preserving Tech: Passion for privacy and an understanding of data minimization strategies, secure enclaves, or Privacy-Enhancing Technologies (PETs).