Machine Learning Engineer, User & Content Intelligence

Apple Apple · Big Tech · Seattle, WA · Machine Learning and AI

Machine Learning Engineer focused on building foundational capabilities for personalization experiences across Apple's consumer products. The role involves engineering large-scale feature pipelines, architecting training data systems, and optimizing for privacy and scale, bridging edge devices and cloud backends. This is a senior-level role requiring expertise in distributed data processing and serving layers.

What you'd actually do

  1. 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.
  2. Engineer Large-Scale Feature Pipelines: Build robust, petabyte-scale pipelines that ingest and combine disparate data into coherent user profiles and rich content representations.
  3. Architect Training Data Systems: Transform raw data into the high-value features that train our next-generation ML models. Architect the systems that generate this data and seamlessly integrate it with our training infrastructure.
  4. Optimize for Privacy & Scale: Build highly optimized stacks that extend existing data systems into privacy-constrained environments. Implement data minimization strategies to securely leverage rich user features without compromising trust.
  5. Cross-Functional Innovation: Partner closely with data systems teams, core compute engineers, and ML teams to ensure the right context is delivered to the right compute environment at the exact right time.

Skills

Required

  • BS or MS in Computer Science, Data Engineering, Software Engineering, or a related field.
  • Proven track record of shipping complex, large-scale data engineering, feature serving, or machine learning systems to production.
  • Expertise in designing distributed data processing systems (e.g., Spark, Flink).
  • Expertise in building low-latency, high-throughput data serving layers or Feature Stores.
  • Deep proficiency in Java or Go for building high-performance production backend systems.
  • Proficiency in Python for model training ecosystems.
  • Demonstrated experience thinking critically about data architecture, including data ontology, discoverability, and bridging distributed data sources.

Nice to have

  • Experience building systems that bridge cloud backend systems with on-device or edge compute environments.
  • Familiarity with generating, managing, and serving dense embeddings for retrieval, ranking, and personalization systems.
  • Experience building feature stores, data catalogs, or implementing compliance-by-design in a regulated environment.
  • Understanding of data minimization strategies, secure enclaves, or Privacy-Enhancing Technologies (PETs).

What the JD emphasized

  • Senior-Level Experience: A proven track record of shipping complex, large-scale data engineering, feature serving, or machine learning systems to production.
  • Mastery 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.
  • Strong Software Engineering: Deep proficiency in Java or Go for building high-performance production backend systems, and Python for model training ecosystems.

Other signals

  • building foundational capabilities for personalization
  • engineering high-performance stacks for feature transformation
  • architecting training data systems for ML models
  • optimizing for privacy and scale in data processing
  • shipping complex, large-scale data engineering systems