Senior Engineering Manager, Apple Data Platform

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Senior Engineering Manager to lead a team building foundational systems and services for Apple's AI and Data governance platform. The role focuses on Big Data management, ML infrastructure, and Generative AI, enabling efficient and scalable model development while ensuring regulatory compliance. It involves leading efforts in data lake governance and GenAI infrastructure across the entire model development lifecycle, influencing ML practitioners and product development.

What you'd actually do

  1. Lead, mentor, and grow a high-performing team of software engineers, and technical leads.
  2. Set and communicate a clear long-term technical vision aligned with business priorities, and guide short- and mid-term execution toward that vision.
  3. Drive the design, delivery, and operation of large-scale data and AI governance infrastructure.
  4. Partner closely with Legal, Compliance, ML engineers, data scientists, researchers, and cross-functional stakeholders to define high-impact capabilities and deliver them with quality and reliability.
  5. Own reliability, scalability, and operational excellence, including defining success metrics and continuously improving availability and performance.

Skills

Required

  • Leadership in infrastructure and distributed systems
  • Strategic thinking and effective execution in complex, cross-functional environments
  • Experience building world-class platforms at scale
  • Comfortable operating across diverse infrastructure environments
  • Proven ability to define and execute a forward-looking technical vision
  • Strong understanding of emerging trends in AI, generative models, and data-centric machine learning
  • Strong understanding of governance and compliance trends and regulations for data and AI
  • Demonstrated experience delivering large-scale distributed systems and ML/data infrastructure into production environments
  • Strong track record of leading, mentoring, and scaling high-performing infrastructure and platform teams
  • Deep passion for building reliable, scalable systems with high availability, strong performance, and an excellent developer experience
  • Experience navigating complex, cross-functional environments and managing expectations across multiple stakeholders and partner teams
  • Excellent communication skills, with the ability to clearly articulate technical strategy, trade-offs, and impact to diverse audiences, including senior leadership
  • Proven ability to partner effectively with recruiting to attract, assess, and grow top technical talent
  • Strong business acumen and results-driven mindset
  • Comfortable operating in ambiguity, taking initiative, and leading teams through fast-paced, evolving problem spaces
  • B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience

Nice to have

  • Experience building platforms that enable Data/AI governance and/or Responsible AI at scale
  • Experience designing or operating high-performance data access paths for ML training and supervised fine-tuning
  • Background working with ML practitioners, data scientists, and researchers to translate research needs into scalable production systems
  • Experience operating ML Ops infrastructure across heterogeneous environments, including on-prem, hybrid, or multi-cloud deployments
  • Strong perspective on where ML platforms and AI infrastructure are headed, and the ability to adapt platform strategy as the ecosystem evolve

What the JD emphasized

  • strong understanding of governance and compliance trends and regulations for data and AI
  • Demonstrated experience delivering large-scale distributed systems and ML/data infrastructure into production environments
  • Strong track record of leading, mentoring, and scaling high-performing infrastructure and platform teams
  • Experience building platforms that enable Data/AI governance and/or Responsible AI at scale
  • Experience designing or operating high-performance data access paths for ML training and supervised fine-tuning

Other signals

  • enabling teams to move from data to trained models efficiently, reliably, and at scale
  • governance of large scale data-lakes, and GenAI infrastructure spanning the entire GenAI model development lifecycle
  • ensure compliance with the latest AI regulations while accelerating AI/ML development and experimentation
  • shape how data and machine learning is developed, deployed, and governed at scale