Engineering Manager, User & Content Intelligence

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

Engineering Manager to lead a team building foundational ML systems for personalization on Apple's consumer platforms. The role involves managing ML engineers, defining technical strategy for distributed feature access and pipelines, and ensuring data quality and privacy compliance at petabyte scale. It bridges data engineering, ML systems, and privacy, focusing on the data layer that powers intelligent experiences.

What you'd actually do

  1. Hire, mentor, and grow a high-performing team of ML engineers. Foster a culture of technical excellence, innovation, and deep respect for user privacy.
  2. Define the roadmap and architectural vision for distributed feature access (spanning device and cloud), large-scale feature pipelines, and robust feature stores. Guide technical decisions around distributed data processing (Spark, Flink), production backend services (Java, Go), and model training integration (Python).
  3. Act as the primary bridge between your team and key partners, including ML teams, Core Compute, Data Systems, and Privacy/Legal teams. Ensure seamless integration of data capabilities across device and cloud boundaries.
  4. Oversee the design, deployment, and operation of petabyte-scale pipelines and low-latency serving stacks. Establish rigorous standards for system reliability, data quality, and compliance-by-design.
  5. Advocate for and implement data minimization strategies and privacy-preserving architectures, ensuring that all data products meet the highest global standards for security and user trust.

Skills

Required

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

Nice to have

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

What the JD emphasized

  • strict privacy compliance
  • privacy compliance
  • privacy
  • privacy-preserving architectures
  • privacy-preserving tech
  • privacy-enhancing technologies (PETs)

Other signals

  • building foundational capabilities for personalization
  • processing and delivering user and content features for personalization
  • transforming raw data into governed, discoverable intelligence
  • managing petabyte-scale data engineering, ML systems, and privacy compliance