Machine Learning Engineer – Recommendations & Personalization (Feature Engineering)

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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About this role

Machine Learning Engineer – Recommendations & Personalization (Feature Engineering) Seattle, United States of America Apple Services Engineering embodies Apple's deep commitment to uniting creativity with technology. Our team powers flagship services-including the App Store, Games, Apple Arcade, Apple TV, Apple Music, Apple Podcasts, and Apple Books-delivering world-class entertainment and experiences to users worldwide across a diverse set of global languages. Through relentless pursuit of excellence and innovation at scale, we consistently meet Apple's high standards for quality and performance. Our engineers design and scale the machine learning systems that make Apple's services feel uniquely personal. We are now pioneering the next generation of recommendation architectures - blending traditional ranking models with cutting-edge generative and agent-driven intelligence to create adaptive, context-aware, and delightful user experiences. If you are excited about advancing recommendation technology at massive scale - and about exploring how Large Language Models (LLMs), advanced retrieval, and modular ML systems can reshape personalization - we'd love to meet you. As a Machine Learning Engineer specializing in Recommendations & Personalization, you will be a pivotal contributor at the intersection of robust ML infrastructure, innovative recommendation systems, and emerging generative AI technologies. You will design, optimize, and deploy end-to-end recommendation flows - spanning sophisticated feature engineering, model training, real-time inference, and feedback loops. Simultaneously, you will prototype and build next-generation LLM-powered and agentic recommendation concepts that push the boundaries of what's possible. You will partner closely with applied researchers, infrastructure engineers, and data scientists to bring both production-grade ML systems and exploratory

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