The Apple Services Engineering AI/ML organization is looking for a Machine Learning Engineer to help build next-generation media discovery experiences for Apple's ground breaking devices and platforms, leveraging Artificial Intelligence & Machine Learning at scale.
Wonder how Apple's Media Products show relevant and rich Discovery experiences covering search, browse and recommendations for Apple's media offerings - including App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books? Come join us! Design and develop AI/ML driven Discovery features for billions of Apple users worldwide! Propose, prototype and evaluate algorithm improvements that power these rich experiences. Evangelize and build reusable AI capabilities to enhance the foundation of how these user facing features are built in the larger organization.
The Apple Services Engineering (ASE) organization is one of the most exciting examples of Apple’s long-held passion for combining art with 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 40 languages to more than 170 countries. Our scientists and engineers build secure, end-to-end solutions powered by Artifical Intelligence & Machine Learning. Thanks to Apple’s unique integration of hardware, software, and services, designers, scientists and engineers in ASE 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
This is a team with strong expertise in Information Retrieval, Machine Learning, Language Modeling, Generative AI, Data Mining, and Distributed Computing (Hadoop, Scala, Spark).
You will drive technical advancement, influence product direction, and be part of the team responsible for bringing the latest advancements in Machine Learning, Natural Language Processing and Generative AI to drive major impact on how users discover Apple Media content on devices worldwide!
You will use data driven analysis to ideate, evaluate and prioritize features, and conduct A/B Tests to ensure we objectively measure improvements. You will ensure successful delivery of features, code, data, and models to production. You will collaborate with researchers, engineers, and operations teams to ensure that features and models are functioning at or above expected performance levels, globally in languages from Arabic to Vietnamese and everything in between!
Minimum Qualifications
BS in Computer Science, Computer Engineering, Information Systems, Electronic Engineering or related fields with 4+ years of experience working in the AI/ML field.
Strong programming skills in Java, Scala, Python and experience with ML libraries such as PyTorch, TensorFlow, Hugging Face, LangChain, or similar.
Solid experience and understanding of modern ML architectures, big data pipelines, and evaluation techniques.
Preferred Qualifications
MS/PhD in Computer Science, Computer Engineering, Information Systems, Electronic Engineering or related fields with 2+ years of experience working in the AI/ML field.
Expertise in large-volume big data processing (batch or streaming) and experience with Apache Spark and Apache Flink .
Familiarity with agentic workflows, Retrieval-Augmented Generation (RAG), vector databases (e.g., FAISS, Pinecone), and knowledge graphs.
Strong communication skills and adept at working with cross functional partners to design cohesive engineering solutions that scale ML products for billions of users.
Responsibilities
Inference Service Development: Design, develop, and deploy high-performance machine learning inference services with a focus on scalability and efficiency.
Big Data Pipeline Engineering: Build and maintain data processing pipelines to support model training and tuning across large datasets.
Model Training Pipeline Optimization: Manage and optimize GenAI & Machine Learning training pipelines to improve performance in distributed computing environments.
Areas of focus: Work on implementation of applications such as summarization, question answering, chatbots, information retrieval, semantic search, and text generation.
Eeo Content
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