Sr. Engineering Manager, ASE Efficiency

Apple is seeking a Senior Engineering Manager to lead Demand Management within Apple Services Engineering (ASE) Efficiency group. You will build the engineering team, software platforms, and practices that help large-scale services understand, validate, and optimize infrastructure demand while protecting reliability, performance, and scalability. This is a hands-on leadership role. You will work alongside engineers on service architectures, demand models, forecasting, telemetry, and capacity decisions, and connect technical findings to infrastructure investment priorities.

ASE Efficiency is building a closed-loop approach to infrastructure efficiency across three functions: Demand Management, Forecasting, and Foundation. Demand Management connects application and business needs to validated, explainable infrastructure requirements. Your team will partner with service owners to understand architectures, dependencies, workload behavior, product launches, and growth. You will translate these inputs into resource requirements and bills of materials (BOMs) across compute, memory, storage, networking, and shared platforms, accounting for inventory, utilization targets, resilience, and operational risk. You will own the strategy and execution of Demand Management across multiple organizations and lines of business. Working with Forecasting and Foundation, you will connect validated demand to forecasts, trusted telemetry, and planning tools, then use actual utilization and service outcomes to improve future decisions. Your team will make the drivers, assumptions, risks, and tradeoffs behind infrastructure investments clear to engineering and business leaders.

Minimum Qualifications

• 10+ years of software engineering, infrastructure engineering, SRE, capacity engineering, or related technical experience.
• 5+ years of engineering management experience, including managing managers and/or senior technical leaders, with a track record of hiring, developing, and retaining engineering talent.
• Experience leading engineering teams responsible for large-scale distributed systems, cloud infrastructure, platform engineering, or infrastructure management.
• Strong software engineering background and experience building or operating production systems, platforms, tooling, or automation used by multiple engineering organizations.
• Ability to reason deeply about application architectures and distributed systems across compute, memory, storage, networking, databases, and containerized environments, and connect workload behavior to infrastructure requirements.
• Strong analytical judgment and experience using telemetry, utilization, and performance data to validate requirements and guide engineering decisions.
• Demonstrated ability to set technical strategy, establish engineering standards, and drive adoption across organizational boundaries.
• Excellent written and verbal communication, with experience influencing senior engineering, infrastructure, finance, and business stakeholders and explaining technical and financial tradeoffs.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience.

Preferred Qualifications

• Experience leading capacity management, infrastructure efficiency, performance engineering, demand forecasting, FinOps, or resource management teams.
• Experience connecting product roadmaps, feature launches, customer growth, and business forecasts to infrastructure planning for large-scale consumer or cloud services.
• Experience developing capacity modeling, forecasting, automated sizing, optimization, or infrastructure intelligence platforms.
• Experience with Kubernetes, distributed data systems, storage platforms, traffic management, or observability systems, including data quality and attribution across shared infrastructure.
• Understanding of infrastructure economics and supply chains, including capital investment, cloud spend, cost attribution, procurement, hardware deployment, and data center constraints.
• Experience applying machine learning, GenAI, or LLM-assisted tooling to infrastructure analysis, automation, forecasting, or engineering productivity.