Senior Engineering Manager, Apple ML Data Platform at Apple

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Mar, 26

Salary

0.0

Posted On

18 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data-Centric ML, Generative AI, Distributed Systems, Infrastructure, Data Governance, Synthetic Data Generation, Data Transformation, Visualization, High-Performance Data Access, Cross-Functional Collaboration, Technical Strategy, Leadership, Communication, Business Acumen, Problem Solving

Industry

Computers and Electronics Manufacturing

Description

We are looking for a Senior Engineering Manager to lead teams building foundational platforms that power large-scale machine learning. This role sits at the intersection of ML infrastructure, data-centric ML, and generative AI, enabling teams across the company to move from data to trained models efficiently, reliably, and at scale. You will lead efforts that unify ML dataset creation and sharing with built-in governance, accelerate model quality through intelligent data workflows, and deliver high-performance data access for training and experimentation. Your work will directly influence how ML practitioners develop, optimize, and scale models across a wide range of products and services. You will join a team at the forefront of ML infrastructure and generative AI, where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. DESCRIPTION As a Senior Engineering Manager in the ML Data group, you will lead the design and delivery of core platforms that support the full ML lifecycle, from experimentation to large-scale training and deployment. These platforms enable teams to create and share ML datasets with governance provided out of the box, improve training outcomes through data-centric capabilities such as synthetic data generation, rapid data transformation, and visualization, and efficiently load data at scale for modern accelerators. Your teams will build and operate systems that support multimodal data, including text, images, audio, and more, while ensuring data access remains efficient and predictable as workloads grow. This includes designing and scaling high-performance data paths that support streaming, random access, sharding, and high-throughput sequential reads to keep training pipelines performant and GPUs fully utilized. 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 ML researchers, 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 building world-class ML platforms at scale, comfortable operating across diverse infrastructure environments, and excited to work at the intersection of cutting-edge ML research and production systems. This is a unique opportunity to shape how machine learning is developed, deployed, and scaled across the company, 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. * 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. * Proven ability to partner effectively with recruiting to attract, assess, and grow top technical Excellent communication skills, with the ability to clearly articulate technical strategy, trade-offs, and impact to diverse audiences, including senior leadership. 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 leading platforms that support data-centric ML, foundation models, or generative AI workloads at scale. * Familiarity with multimodal data systems spanning text, images, audio, video, and embeddings. * Experience designing or operating high-performance data access paths for ML training, including streaming, sharding, random access, and large-scale sequential reads. Background working with ML practitioners, data scientists, and researchers to translate research needs into scalable production systems. Experience operating ML infrastructure across heterogeneous environments, including on-prem, hybrid, or multi-cloud deployments. Exposure to governance, lineage, and compliance considerations in large-scale data and ML platforms. Strong perspective on where ML platforms and AI infrastructure are headed, and the ability to adapt platform strategy as the ecosystem evolve

Responsibilities

Lead the design and delivery of core platforms that support the full machine learning lifecycle, from experimentation to large-scale training and deployment. Ensure efficient data access and governance while improving training outcomes through data-centric capabilities.