Machine Learning Engineer, Apple Services Engineering

Wonder how Apples Media Products show relevant search results and recommendations across Apples media offerings - including App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books? Come join us! Design, build, and deploy recommendation models that personalize the App Store for billions of users worldwide! 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 Apples long-held passion for combining art and technology. We are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, Games and Apple Fitness+. And we do it on a massive scale, meeting Apples high expectations with high performance, to deliver a huge variety of entertainment in over 35 languages to more than 150 countries.

Our researchers and engineers build secure, end-to-end solutions powered by machine learning. Thanks to Apples 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 Apples privacy policy, one of Apples core values. Although services are a bigger part of Apples business than ever before, these teams remain small, flexible, and multi-functional, offering greater exposure to the array of opportunities here. We are looking for an exceptional Machine Learning Engineer to help us design, build, and ship recommendation models that power personalization across the App Store. With your expertise, we want to develop novel 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 Apples truly incredible global scale.Design, train, and launch recommendation models that improve relevance and business metrics on key App Store surfaces. Own ranker modules end-to-end: candidate representation, feature engineering, model architecture, offline evaluation, A/B experimentation, and production monitoring. Drive adoption of state-of-the-art ranking techniques from research prototypes into production. Partner closely with ML researchers, data engineers, and infrastructure teams to productionize new modeling approaches with high reliability and low latency. Ship production-quality code and drive engineering best practices, system architecture, and code quality within the team. Help chart the future growth of personalization across Apples services ecosystem.Bachelors and Masters in a quantitative field, including Computer Science, Mathematics, Statistics, Physics, etc. 3+ years of relevant work experience. Hands-on experience with production-level recommender systems. Deep knowledge of recommendation systems, design patterns and tools, with particular depth in deep-learning architectures and multi-task modeling. Proven track record of shipping recommendation models to production at scale, with a strong understanding of the constraints of serving billions of users. Knowledge of modern recommendation architectures across retrieval and ranking, multi-task learning, sequence and transformer-based models, generative recommenders, and multi-objective optimization. Proven grasp of the open-source Python ML/AI tech stack, including TensorFlow, PyTorch, scikit-learn, numpy-scipy-pandas. Familiarity with big data technologies and distributed computing (e.g., Spark, Hadoop, Kafka). Familiarity with using LLM-powered tools (coding and research assistants) to accelerate day-to-day engineering and research workflows. Strong written & oral communication skills. PhD in a quantitative field, including Computer Science, Mathematics, Statistics, Physics, etc.