Senior Engineering Manager, Data Platform

Position: Senior Engineering Manager, Apple Data Platform
** Role Number:*
* ** Summary*
* We are looking for a Senior Engineering Manager to lead the team building foundational systems and services that power Apple's AI and Data governance platform. This role sits at the intersection of Big Data management, ML infrastructure, and Generative AI, enabling teams across the company to move from data to trained models efficiently, reliably, and  will lead efforts that enable governance of large scale data-lakes, and GenAI infrastructure spanning the entire GenAI model development lifecycle.

Your work will ensure compliance with the latest AI regulations while accelerating AI/ML development and experimentation. You will directly influence how ML practitioners develop and scale models across a wide range of products and services within Apple.

** Description*
* As a Senior Engineering Manager in the AI and Data Governance group, you will lead the design and delivery of core systems and services that enable governance of Apple's data management and machine learning solutions, spanning the full ML lifecycle - from experimentation to large-scale training and deployment.
This role requires strong leadership in infrastructure and distributed systems, paired with strategic thinking and effective execution in complex, cross-functional environments. You will work closely with Legal, Compliance, ML engineers, platform and infrastructure teams, and product partners to align on requirements, set technical direction, and deliver multi-quarter initiatives with broad organizational impact. We are looking for an experienced leader who is passionate about Governance, Big Data, and AI, with an experience of building world-class platforms at scale, and comfortable operating across diverse infrastructure environments.

This is a unique opportunity to shape how data and machine learning is developed, deployed, and governed at scale within Apple, with the autonomy to experiment, the scale to make meaningful impact, and the support to take ideas from concept to production.

** Minimum Qualifications*
* + Proven ability to define and execute a forward-looking technical vision, with a 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, with the ability to balance long-term strategic investments with near-term delivery.

+ 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.

** Preferred Qualifications*
* + 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.