Sr. ML Optimization Engineer, ASE Efficiency
Confirmed on the employer's own hiring board on Nov 10, 2026. First seen by Alion on May 12, 2026. Apple scores A on the Alion truth index.
Across Apple Services Engineering (ASE), efficiency is not just a technical goal; it's an essential part of our commitment to environmental sustainability and optimal resource utilization. This team plays a pivotal role in ensuring that our cloud services are not only robust and reliable but also efficiently utilize resources at scale. This team also focuses on ML-driven forecasting, capacity planning, resource optimization, and the development of cost models across ASE’s large-scale services. As a Sr. ML Optimization Engineer, you will work at the intersection of systems engineering, infrastructure strategy, applied analytics, machine learning, and large-scale optimization. You will have the unique opportunity to collaborate closely with experts, contribute to cutting-edge forecasting and optimization strategies, and directly impact millions of users globally. Our team is at the forefront of driving efficiency in one of the world’s largest cloud infrastructures, supporting billions of devices globally.
Description
The Sr. ML Optimization Engineer will be a key player in Apple’s ongoing efforts to improve the efficiency of its cloud services. This role involves in-depth analysis of infrastructure usage patterns, and data-driven capacity planning to ensure scalability and reliability while minimizing resource wastage. You will connect application and business demand to infrastructure requirements, then use measured utilization to improve forecasting and optimization decisions.
Minimum Qualifications
3+ years of relevant experience in large-scale cloud services or similar environments
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 the ability to communicate complex ideas clearly and effectively to cross-functional teams
Hands-on experience with platform observability tools, enabling deep insights into service performance and helping to drive optimizations
Experienced in developing cost models and strategies for software services, ensuring optimal resource utilization and cost-effectiveness
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
Preferred Qualifications
Experience applying classical optimization techniques to real-world systems or infrastructure problems
Demonstrable experience in capacity planning and resource optimization using machine learning and/or optimization techniques
Knowledge of machine learning pipelines and service monitoring tools a plus
PhD in CS or related field with a focus on machine learning, optimization, or large-scale distributed systems