Apple
Manager, ASE Efficiency
$226K–$338K
San Diego, California · Manager · Full-time · Posted Nov 2, 2026
- Experience
- 7+ years of relevant experience in large-scale cloud services or similar environments; 2+ years of engineering management experience.
- Education
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. PhD in CS or a related field focused on machine learning, optimization, or large-scale distributed systems is preferred.
About the job
Leads efforts to improve Apple Services Engineering cloud efficiency through capacity planning, forecasting, and large-scale resource optimization.
What you'll do
- Lead and help optimize the design, deployment, and operation of services across ASE.
- Partner with service, infrastructure, SRE, planning, data science, and finance teams on sustainability and resource-utilization strategies.
- Develop optimization solutions for capacity allocation, workload scheduling, and cost reduction, integrating them into reusable software and tooling.
What they ask for
- Strong software engineering background and experience with ML-based modeling, time-series forecasting, and data-driven decision-making for cloud services.
- Exceptional analytical and problem-solving skills, with clear communication across teams.
- Hands-on experience with platform observability tools and developing cost models and strategies for software services.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Nice to have
- Experience applying classical optimization techniques to real-world systems or infrastructure problems.
- Experience in capacity planning and resource optimization using machine learning and/or optimization techniques; knowledge of ML pipelines and service monitoring tools is a plus.
Pay
$225,600 and $338,400 USD base salary
Tools
ML-based modeling, time-series forecasting, platform observability tools, machine learning pipelines, service monitoring tools
AI focus
Applies machine learning to forecasting, capacity planning, and resource optimization for cloud services.
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