Apple
Machine Learning Engineering Manager - Ads Recommendations
$206K–$356K
Cupertino, California · Manager · Full-time · Posted Aug 12, 2026
- Experience
- 8+ years in machine learning, statistics, and quantitative optimization; at least 2–3 years as a tech lead or at least 1 year as an engineering manager. Preferred: 5+ years leading teams applying advanced statistical/ML methods.
- Education
- BS, or equivalent experience, in AI, ML, Mathematics, Computer Science, or a related field.
About the job
Leads engineering strategy and a team building scalable, privacy-compliant advertiser recommendation systems for Apple Ads, while shaping related campaign and auction optimization capabilities.
What you'll do
- Set technical strategy and deliver the next-generation recommendation engine, including scalable models and intelligence systems.
- Guide experimentation, deployment, monitoring, and performance improvements for advertiser-facing solutions.
- Lead, mentor, and grow engineers; partner with business and engineering teams on strategy and execution.
What they ask for
- 8+ years in machine learning, statistics, and quantitative optimization, with at least 2–3 years as a tech lead or at least 1 year as an engineering manager.
- Track record leading machine learning engineering teams through design and implementation, ensuring scalability and reliability.
- Experience with deployment, monitoring, system management, A/B tests and pilots for sales / advertiser-facing applications; experience building GenAI and agentic systems.
- Production experience with Python or Java; proficiency in databases, SQL, and scripting languages for data processing and model deployment.
Nice to have
- 5+ years leading teams applying advanced statistical/ML methods; online advertising experience.
- MS/PhD, or equivalent experience, in AI, ML, Mathematics, Computer Science, or a related field.
Pay
$206,200 and $356,400 USD base salary
Tools
GenAI, Python, Java, databases, SQL
AI focus
Build an advertiser recommendation engine using machine learning, GenAI, and agentic approaches; support campaign and auction optimization.
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