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Senior Engineering Manager - Apple Data Platform - Platform Efficiency
Apple
H1B ✓On-sitemanagerPosted April 20, 2026
About the Role
You will lead engineering teams focused on the unit economics and financial engineering of Apple's Data Platform. Your role involves designing high-performance systems to ensure global infrastructure efficiency across multi-cloud and on-premises environments.
Requirements
Candidates must have over 10 years of experience in large-scale data/AI infrastructure and 3-5 years of engineering management experience. A strong technical background in Kubernetes, Spark, and Ray, along with a degree in Computer Science or a related field, is required.
Full Job Description
Apple Data Platform (ADP) serves as a foundational building block for Apple’s Services, Software, and AI/ML ecosystem, providing advanced data governance, compliance, and scalable data management. It delivers a comprehensive suite of capabilities, including batch and real-time data processing, embeddings management, feature stores, lakehouse architecture, data virtualization, and inference-as-a-service. These technologies empower analytics and AI workflows across Apple’s ecosystem, enabling seamless integration and innovation across products and services. Leveraging open-source technologies such as Ray, Spark, Flink, and Iceberg across multi-cloud and on-premises environments, ADP powers data-driven intelligence that continuously enhances the customer experience.
As a Senior Engineering Manager, you will lead the engineering teams responsible for the unit economics and financial engineering of the ADP. You will be a hands-on technical leader, driving the design and operation of high-performance systems that provide deep visibility, cost attribution, and capacity optimization for massive-scale data and AI workloads. Your mission is to ensure that our global footprint spanning first-party data centers and multiple public clouds (AWS, GCP, etc.) is as economically efficient as it is technically performant. You will bridge the gap between infrastructure engineering and operational accountability, translating complex resource consumption patterns into optimized platform configurations at Apple scale.
10+ years of experience building and operating very large-scale data/AI infrastructure in production environments. 3-5+ years of engineering management experience, with a track record of leading technical teams in the infrastructure space. Hands-on Multi-Cloud Experience: Proven ability to optimize workloads across multiple environments (e.g., AWS, GCP, and on-premises/first-party). Systems Engineering Background: Strong technical grasp of Kubernetes, Spark, and Ray, with the ability to dive into resource scheduling and platform internals. Data-Driven Impact: Proven ability to deliver measurable TCO improvements through architectural changes and automated governance. Education: BS or MS in Computer Science, Electrical Engineering, or a related field.
Advanced Capacity Modeling: Experience building tools for proactive capacity forecasting in high-growth AI environments. Deep Profiling Skills: Expertise in identifying resource bottlenecks at the system level (CPU, Memory, I/O) to drive unit cost reduction. FinOps Implementation: Experience applying FinOps principles to large-scale, multi-tenant container platforms.
Description
As a Senior Engineering Manager, you will lead the engineering teams responsible for the unit economics and financial engineering of the ADP. You will be a hands-on technical leader, driving the design and operation of high-performance systems that provide deep visibility, cost attribution, and capacity optimization for massive-scale data and AI workloads. Your mission is to ensure that our global footprint spanning first-party data centers and multiple public clouds (AWS, GCP, etc.) is as economically efficient as it is technically performant. You will bridge the gap between infrastructure engineering and operational accountability, translating complex resource consumption patterns into optimized platform configurations at Apple scale.
Minimum Qualifications
10+ years of experience building and operating very large-scale data/AI infrastructure in production environments. 3-5+ years of engineering management experience, with a track record of leading technical teams in the infrastructure space. Hands-on Multi-Cloud Experience: Proven ability to optimize workloads across multiple environments (e.g., AWS, GCP, and on-premises/first-party). Systems Engineering Background: Strong technical grasp of Kubernetes, Spark, and Ray, with the ability to dive into resource scheduling and platform internals. Data-Driven Impact: Proven ability to deliver measurable TCO improvements through architectural changes and automated governance. Education: BS or MS in Computer Science, Electrical Engineering, or a related field.
Preferred Qualifications
Advanced Capacity Modeling: Experience building tools for proactive capacity forecasting in high-growth AI environments. Deep Profiling Skills: Expertise in identifying resource bottlenecks at the system level (CPU, Memory, I/O) to drive unit cost reduction. FinOps Implementation: Experience applying FinOps principles to large-scale, multi-tenant container platforms.