Machine Learning Engineer, Apple Intelligence Data Platform - Proactive

Are you excited about building innovative generative AI experiences that empower millions of users daily? Do you thrive in collaborative environments and enjoy applying your machine learning expertise to real-world user experiences? If so, we’d love to hear from you. We’re looking for a Senior Machine Learning Engineer to join the Apple Intelligence Data Platform team. \\n\\nThis team powers key intelligence features across Apple’s ecosystem — including Siri Suggestions, the Shortcuts app, and more — by building scalable, privacy-conscious ML systems! Our team is responsible for the Research, Development and Deployment of the core platform that powers Apple Intelligence on device and on private compute cloud — adapters, speculative decoding, guided generation to name a few. \\n\\nOur team is responsible for underpinning all the Apple Intelligence features we shipped to our customers including Writing Tools, Notification Summaries, Siri and more. Our team has a great mix of talent across Machine Learning and Software Engineering. We love to share our knowledge within our team, stay abreast of state-of-the-art and deliver outstanding products for our users. We also have a strong culture of multi-functional collaboration across teams at Apple. Proactive Intelligence is central to Apple Intelligence. \\n\\nWe’re building a contextual, on-device platform that anticipates user needs — from predicting which app you"ll launch next to understanding the difference between work and leisure modes. This is your opportunity to help shape the next generation of personal, privacy-preserving intelligence for millions of users! \\n\\nhttps://www.apple.com/apple-intelligence/ (https://www.apple.com/apple-intelligence/)\\n

As an engineer on the Apple Intelligence Data Platform team, you will work on developing and integrating foundational components for on-device and cloud-based intelligence. You will focus on designing, building, and deploying scalable agent systems that understand the user’s context and personal knowledge. Your work will directly influence how users interact with Apple products through on-device search as well as through context-aware, proactive, and personalized experiences. \\n\\nYou will work on building the foundational platforms that personalize the on-device Siri assistant and sync the Siri and Apple Intelligence experience across the Apple device lineup — including iPhone, iPad, and Mac. This role is ideal for candidates with hands-on experience in Vector databases, Knowledge Graphs, Semantic Search, Retrieval-Augmented Generation (RAG), decoding strategies, Generative AI inference, and prompt optimization. You will collaborate closely with several engineering teams at Apple —such as Accessibility, Hardware, Human Interface, NLP, Privacy, etc- to power exciting Apple Intelligence features and ship them to our customers.\\n

5+ years of increasing responsibility and relevant experience\\nBachelor"s degree or higher in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Information Retrieval or a related field.\\nExperience in building on-device platforms, data pipelines and frameworks.\\nExperience in one or more of the following: Knowledge Graphs (KG), RAG systems, integration of LLMs with external memories, Vector databases and related fields.\\nExperience supporting data analytics and instrumentation for large-scale ML / AI systems\\nDeep understanding of machine learning and deep learning algorithms\\nExperience designing and optimizing runtime or inference systems for machine learning and deep learning models in production\\nExcellent software design, problem solving, critical thinking and collaborative skills including written and verbal communication\\nProficiency in one or more of the following languages: Python, Go, Java, C++, or Swift\\nAbility to understand/clarify product requirements and translate them into technical tasks in ML modeling and engineering\\n

MS or Ph. D in Computer Science or a related field\\nExperience in LLM, machine learning models, deep learning models, information retrieval, platform development, or natural language processing\\nExperience building offline experimentation, training, and evaluation pipelines to iterate on ML model performance and accuracy\\nStrong analytical and independent problem-solving skills\\nExperience working in cross-functional teams across product, design, and infrastructure\\n