Apple Ads is seeking a self-motivated individual to build the next generation of ads platforms, focusing on delivering relevant and high-quality ad experiences while maintaining a healthy marketplace. The role involves developing and implementing machine learning and optimization algorithms, particularly in the ads space. The ideal candidate will thrive in Agile environments and have a strong understanding of scalable architectures. This position offers the opportunity to drive optimal campaign performance and enhance the advertiser experience on a global scale.

• 5+ years of experience building machine learning and quantitative optimization capabilities at scale.
• Experience in machine learning, quantitative methods, control systems, or reinforcement learning.
• Deep fluency in Java or Python.
• Experience with Spark, Hadoop, or other distributed frameworks.
• PhD in Machine Learning, Statistics, Control Theory, Forecasting, Optimization, Reinforcement Learning, or related field, or equivalent industry experience.

• Design and build scalable solutions for advertisers to optimize campaign goals and performance.
• Implement research concepts into production quality code.
• Define clear, testable research hypotheses with intended business impact.
• Formulate and advocate for R&D objectives to cross-functional team members.
• Contribute to and review research for top conferences and publications.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.