Machine Learning Engineer, Apple Services Engineering

Apple

Seattle, WA

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JOB DETAILS

SKILLS

Algorithms, Apache Hadoop, Apache Kafka, Apache Spark, Apple, Artificial Intelligence (AI), Big Data, Communication Skills, Computer Science, Data Management, Data Processing, Deep Learning, Design Patterns Programming Methodologies, Distributed Computing, Fitness, Game Software, Hardware-Software Integration, Large-Scale Systems, Machine Learning, Mathematics, Music, Open Source, Podcasting, Presentation/Verbal Skills, Prototyping, Python Programming/Scripting Language, Search Ranking, Software Development, Software Engineering, Team Player, Writing Skills

LOCATION

Seattle, WA

POSTED

7 days ago

**Weekly Hours:** 40 **Role Number:** 200663898-3337 **Summary** Wonder how Apple's Media Products show relevant search results and recommendations across Apple's media offerings - including App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books? Come join us! Design, build, and deploy machine learning pipelines that personalize the App Store for billions of users worldwide! Prototype, scale, and optimize algorithm improvements. Build robust, large-scale personalized recommender systems for Apps, Games, Videos, Podcasts and Fitness. See your work touch the lives of billions of Apple users worldwide. 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 Fitness+. 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. **Description** We are looking for an exceptional Machine Learning Engineer to help us build and scale personalization systems using the latest advances in machine learning. With your engineering expertise, we want to develop robust, high-performance solutions to power personalized experiences across the App Store that enrich the lives of our customers. You will have the incredible opportunity to partner with researchers to see cutting-edge AI models deployed reliably at Apple's truly incredible global scale. **Minimum Qualifications** + Bachelor's degree in Computer Science, Software Engineering, Mathematics, or a related technical field. + 7+ years of relevant work experience. + Strong software engineering fundamentals and technical competence in production-quality software development. + Real-world experience with building, scaling, and deploying recommendation systems or large-scale ML models. + Proven grasp of the open-source Python AI/ML tech stack, including PyTorch, scikit-learn, and numpy-scipy-pandas. + Solid understanding of machine learning algorithms, design patterns, and tools, including deep learning and generative AI. + Proficiency with big data technologies, data processing pipelines, and distributed computing (e.g., Spark, Hadoop, Kafka). + Experience with ML infrastructure, model optimization, and serving models at scale with low latency. + Strong written & oral communication skills, with a collaborative mindset. **Preferred Qualifications** + Master's degree in Computer Science, Software Engineering, Mathematics, or a related field; OR equivalent practical industry experience. + Industry experience specifically focused on MLOps, recommendation systems, or search ranking infrastructure.

About the Company

Apple

INDUSTRY

Other/Not Classified

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