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. \\n\\nThanks 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. \\n\\nCome 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.
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.\n\nIf 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.
MS or PhD in Computer Science, Software Engineering, or related field.\n8+ 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.\nExperience with big data and stream processing frameworks like Spark, Flink, or Kafka.\nProficiency in object-oriented programming languages such as Java, Scala, or C++.\nExperience building and maintaining large-scale distributed systems for ML workloads.\nDeep understanding of ML model deployment pipelines, runtime optimization, and system integration.\nFamiliarity with A/B testing frameworks, experimental design, and online evaluation.\nStrong focus on system reliability, latency, and observability in production environments.
Experience in batch and real-time inference serving, including autoscaling and traffic management.\nBackground in content recommendation systems, search ranking, or user engagement optimization.\nExperience with CI/CD workflows for ML systems, including safe model rollouts and shadow testing.\nExposure to containerized deployments and orchestration (Kubernetes, Docker).\nExperience building and deploying production-grade applications using LLMs, including expertise in prompt engineering, RAG pipelines, and framework orchestration.\nProven track record of developing autonomous agents capable of multi-step reasoning, external tool integration, and complex task decomposition to solve open-ended problems.\nPrior experience working on consumer-scale media products (apps, games, books, music, or video).
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: c372441dfee81051b07d031e0a1b8d4
- Posted 5 hours ago
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