Engineering Program Manager, AI and Data Platform, Apple Services Engineering

Apple Apple · Big Tech · Seattle, WA · Software and Services

Apple is seeking an Engineering Program Manager to drive the adoption of machine learning and equip engineering teams with robust data insights. This role focuses on shaping and scaling the ML infrastructure and data platform that powers AI application development across Apple Services, supporting the ML lifecycle from data ingestion to model training and product quality enhancement.

What you'd actually do

  1. Drive Program Roadmap and Strategy: Define and execute the roadmap for scalable, privacy-preserving ML infrastructure and data platforms, empowering Apple’s development teams to efficiently ingest, store, process, transform, and analyze data for machine learning and analytics.
  2. Program Ownership and Use Case Identification: Proactively engage with internal partners to identify high-impact use cases, build strong business cases, and define detailed requirements and product specifications.
  3. Lead cross-functional discussions with stakeholders to prioritize and establish a strategic program roadmap.
  4. Establish and Maintain Feedback Loops: Develop and sustain a robust feedback loop with stakeholders, partners, and users to drive iterative improvements, ensuring that products continuously evolve to meet user needs and program objectives.
  5. Define Success Metrics: Develop and monitor key product metrics that align with broader ASE product goals, enabling clear measurement of data platform success and impact.

Skills

Required

  • Program management in ML Infrastructure
  • leading large cross-functional infrastructure programs
  • leading ML, AI, and data programs
  • delivering reliable, large-scale ML infrastructure and data platform products
  • data storage
  • feature store
  • data management
  • data versioning
  • privacy
  • governance
  • analytics
  • backend services
  • high-throughput, user-facing applications
  • cloud technologies
  • building hybrid cloud ML infrastructure and data platforms
  • BS in Computer Science, Computer Engineering, or related technical field OR relevant industry experience

Nice to have

  • optimizing the user experience
  • thought leadership to enhance end-to-end user interactions for technical products
  • ML infrastructure
  • frameworks like PyTorch and TensorFlow
  • Jupyter notebooks
  • Spark
  • Trino
  • Flink
  • SQL
  • Python
  • advanced analytical tools
  • distributed systems
  • large-scale compute environments
  • applied Data & ML infra teams
  • developer platforms
  • APIs
  • infrastructure products

What the JD emphasized

  • ML Infrastructure
  • data platform
  • ML lifecycle
  • program management
  • large-scale ML infrastructure and data platform products

Other signals

  • ML infrastructure
  • data platform
  • ML lifecycle
  • program management