Senior ML Optimization Engineer, iCloud

In Apple’s iCloud services organization, 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 sophisticated cost models for iCloud'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.

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.

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 or Master's degree in Computer Science, Engineering, or a related field

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