About the job
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.
Responsibilities
You will help optimize the design, deployment, and operation of iCloud services.
You will collaborate closely with engineering teams, data scientists, and finance experts to develop and implement strategies that align with Apple’s cloud sustainability and resource utilization principles.
You will formulate and solve large-scale optimization problems to allocate capacity, schedule workloads, and minimize cost under reliability and latency constraints.
Your work will directly contribute to optimizing iCloud’s performance and resource utilization, influencing the unit cost of services and the overall customer experience.
Qualifications
Minimum
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
Preferred
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