ML Engineering Manager, Gen AI Frameworks Team

Apple Apple · Big Tech · New York, NY · Machine Learning and AI

Engineering Manager to lead a team building foundational ML platforms and frameworks for training, evaluation, and deployment of models across Apple services. Focus on scalability, reliability, and developer experience for ML practitioners.

What you'd actually do

  1. Lead and grow an engineering team building core ML frameworks spanning training, evaluation, deployment, and lifecycle management.
  2. Define the technical roadmap for shared ML infrastructure used across SERVICES.
  3. Partner with applied ML, data science, privacy, and product teams to understand requirements and drive adoption.
  4. Drive architectural decisions that prioritize scalability, reliability, privacy, and developer productivity.
  5. Ensure frameworks support best-in-class model evaluation, experimentation, and continuous improvement.

Skills

Required

  • Master's degree in Computer Science or a related field, or equivalent practical experience.
  • 5+ years of professional software engineering experience.
  • 2+ years of people management experience leading engineering teams.
  • Experience building or maintaining production machine learning systems or platforms.
  • Strong background in distributed systems, data-intensive applications, or ML infrastructure.
  • Experience partnering cross-functionally with applied ML or data science teams.

Nice to have

  • Experience building shared ML platforms or frameworks used by multiple teams or organizations.
  • Deep understanding of the ML lifecycle, including training pipelines, evaluation methodologies, and deployment patterns.
  • Experience operating ML systems at scale in production environments.
  • Familiarity with model evaluation, experimentation frameworks, and metrics-driven development.
  • Experience balancing platform abstractions with flexibility for diverse use cases.
  • Strong technical leadership skills, including architecture reviews and long-term roadmap ownership.
  • Demonstrated ability to hire, develop, and retain high-performing engineers.
  • Experience working in environments with strong privacy, security, or compliance requirements.

What the JD emphasized

  • production machine learning systems or platforms
  • ML lifecycle
  • model evaluation
  • operating ML systems at scale
  • strong privacy, security, or compliance requirements

Other signals

  • building foundational ML platforms and frameworks
  • end-to-end machine learning frameworks
  • scale innovation across an entire organization