Sr. Machine Learning Engineer - Recommendations & Personalization (Feature Engineering)

The Apple Services Engineering team is one of the most exciting examples of Apple's long-held passion for combining art and technology. We are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books. And we do it on a massive scale, meeting Apple's high expectations with high performance, to deliver a huge variety of entertainment in over 35 languages to more than 150 countries. Our scientists and engineers build secure, end-to-end solutions powered by machine learning.

Thanks to Apple's unique integration of hardware, software, and services, designers, scientists and engineers here partner to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apple's privacy policy, one of Apple's core values. Although services are a bigger part of Apple's business than ever before, these teams remain small, flexible, and multi-functional, offering greater exposure to the array of opportunities here.

Come join us to build large-scale personalized recommender systems for Apps & Games, Video, Fitness+, Podcast and Books Recommendations. See your work touch the lives of billions of Apple users worldwide.

Description

In this role, you will be responsible for operationalizing machine learning models-from building real-time and batch inference pipelines to optimizing system performance, reliability, and experimentation velocity. You'll help bridge the gap between research and production by developing the infrastructure, tooling, and monitoring required to ship ML-driven features safely and efficiently.

If you are an engineer who enjoys scaling ML solutions, building production-grade services, and driving experimentation across billions of users, this is your opportunity to make a meaningful impact.","responsibilities":"Partner with ML researchers and product teams to transition models into production, ensuring reliability, scalability, and low latency.

Design and implement robust inference services using object-oriented languages (e.g., Java, Scala, C++) that operate at scale across Apple platforms.

Build and manage data pipelines and model execution frameworks to support both batch and streaming use cases.

Develop tooling and infrastructure for model deployment, versioning, rollback, and online evaluation.

Lead A/B testing efforts, including integration, metric tracking, experiment validation, and performance analysis.

Collaborate with infrastructure teams to improve observability, alerting, and model health monitoring.

Drive continuous improvement in latency, throughput, fault tolerance, and overall system reliability.

Preferred Qualifications

Experience in batch and real-time inference serving, including autoscaling and traffic management.

Background in content recommendation systems, search ranking, or user engagement optimization.

Experience with CI/CD workflows for ML systems, including safe model rollouts and shadow testing.

Exposure to containerized deployments and orchestration (Kubernetes, Docker).

Experience building and deploying production-grade applications using LLMs, including expertise in prompt engineering, RAG pipelines, and framework orchestration.

Proven track record of developing autonomous agents capable of multi-step reasoning, external tool integration, and complex task decomposition to solve open-ended problems.

Prior experience working on consumer-scale media products (apps, games, books, music, or video).

Minimum Qualifications

MS or PhD in Computer Science, Software Engineering, or related field.

8+ years of deep software engineering experience, with a strong background in building and deploying production machine learning systems. Experience in areas such as personalization, search, or recommendations is a plus.

Experience with big data and stream processing frameworks like Spark, Flink, or Kafka.

Proficiency in object-oriented programming languages such as Java, Scala, or C++.

Experience building and maintaining large-scale distributed systems for ML workloads.

Deep understanding of ML model deployment pipelines, runtime optimization, and system integration.

Familiarity with A/B testing frameworks, experimental design, and online evaluation.

Strong focus on system reliability, latency, and observability in production environments.

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