Machine Learning Engineer – Recommendations & Personalization; Feature Engineering

Summary

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 generative architectures to life. This is a hands‑on, high‑impact engineering role that bridges robust system design with forward‑looking research and a passion for crafting unparalleled user experiences.

• Pioneer Generative Architectures:
  Collaborate with research teams to prototype, evaluate, and integrate LLM‑driven or generative recommendation architectures, encompassing retrieval, sophisticated ranking, and conversational understanding.
• Build Scalable ML Platform:
  Develop modular ML infrastructure and tooling that accelerates experimentation, safe deployment, and continuous integration—including robust model serving, versioning, rollback strategies, and online evaluation frameworks.
• Craft High‑Performance Services:
  Design, build, and maintain low‑latency, high‑throughput inference services in Go, Rust, Java, Python, or similar programming languages, operating at Apple's immense scale.
• Optimize Recommendation Pipelines:
  Engineer, implement and optimize large‑scale recommendation and personalization pipelines, including both efficient batch processing and ultra‑responsive real‑time serving systems.
• Advance Feature Engineering:
  Design and implement robust data and feature pipelines, including support for online feature stores, streaming updates, and real‑time feature generation.
• Enhance System Reliability:
  Partner with infrastructure teams to elevate system observability, reliability, and performance optimization across critical recommendation workloads.
• Drive Model Evaluation:
  Lead the design and execution of A/B tests and continuous online evaluation of personalization models, ensuring alignment with product goals and measurable user impact.
• Explore Agentic Systems:
  Participate in exploratory initiatives around agentic orchestration frameworks (e.g., Lang Graph, Lang Chain) and their transformative application to adaptive recommendation workflows.

• BS, MS or PhD in Computer Science, Machine Learning, or a related technical field.
• 4+ years of hands‑on experience developing and deploying production‑grade ML systems for personalization, ranking, or recommendation.
• Strong software engineering skills in Go, Rust, Java, Python, or similar languages, with a proven focus on building scalable, high‑performance, and reliable services.
• Extensive experience with distributed data and ML systems (e.g., Ray,…