Apple
Staff Machine Learning Engineer - Ads Bidding & Pacing
$185K–$325K
Cupertino, California · Staff · Full-time · Posted Sep 29, 2026
- Experience
- 8+ years building machine learning and quantitative optimization capabilities across multiple product areas at scale.
- Education
- PhD in Machine Learning, Statistics, Control Theory, Forecasting, Optimization, Reinforcement Learning, or a related field, with experience building production systems; or equivalent experience working with large data science / machine learning projects in industry.
About the job
Build and lead scalable machine-learning and optimization systems for Apple Ads bidding, pacing, campaign performance, and ad recommendations.
What you'll do
- Develop production models, systems, and ad recommendations; run large-scale experiments to assess their effects.
- Lead advanced algorithm development and delivery of capabilities that improve the ad network and differentiate products.
- Work with product, business, and cross-functional partners to set strategy, prioritize an innovation roadmap, and deliver scalable solutions.
What they ask for
- Experience in machine learning, quantitative methods, control systems, or reinforcement learning, and ability to implement research concepts in production-quality code.
- Experience defining testable research hypotheses and knowledge of experimental design and online experimentation.
- Ability to advocate for R&D objectives and results with cross-functional teams, including executive leadership and product management; experience contributing to or reviewing research for top conferences and publications.
- Deep fluency in Java or Python; experience with Spark, Hadoop, or other distributed frameworks.
Nice to have
- Experience in ads optimization, recommendations, or search relevance optimization.
Pay
$184,700 and $324,800 USD base salary
Tools
Java, Python, Spark, Hadoop
AI focus
Apply machine learning, quantitative optimization, control systems, or reinforcement learning to ad bidding, pacing, campaign optimization, and ad recommendations.
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