Machine Learning Engineer - Applied Science

Siri helps hundreds of millions of people find the information they are looking for. A critical part of that mission is helping them quickly find and discover local businesses, places of interest, and addresses. Users rely on us for relevant and easy access to local information like finding a favorite or romantic restaurant, business hours, nearby coffee shop addresses, and directions to prominent locations. The Geo domain team is redefining how hundreds of millions of people use their devices to navigate and explore the physical world around them. We are part of a wider effort to power search across a variety of Apple products - including Siri, Spotlight, Safari, Messages, and more. As part of our team, you will be using innovative machine learning techniques and LLMs in order to understand queries, rank documents, and find useful answers to users' questions. We are looking for an experienced applied researcher with hands-on experience in search and recommendation and deploying powerful machine learning models at scale. You will join a team that combines strong technical skills, product vision, and a love of all things local to bring together the pieces needed to deliver an extraordinary Maps experience in Siri and Spotlight.

As a member of our high-impact, iterative environment, you'll have the unique and rewarding opportunity to shape upcoming products from Apple. Our team includes a diversity of backgrounds from applied scientists with a focus in NLP to experienced distributed systems engineers. We are looking for candidates with both applied machine learning and deep-learning experience as well as strong engineering skills.

You have 8+ years of experience in information retrieval, natural language processing, machine learning, or deep learning.\nYou have a deep understanding of machine learning theory, including supervised learning, ranking models, embeddings, representation learning, and evaluation metrics.\nYou have proven ability to apply advanced ML techniques to improve search relevance and retrieval quality at scale.\nYou are comfortable leading experimentation, offline evaluation, and online A/B testing for iterative improvements in search quality.\nYou actively monitor recent research literature - including arXiv, NeurIPS, ICML, ACL, SIGIR, and industry publications - and have a track record of translating findings into practical system improvements.\nYou independently identify high-impact research directions and drive them forward without requiring top-down direction.\nYou demonstrate a strong bias toward action, moving fluidly from paper to prototype to production in tight iteration cycles - executing quickly while maintaining quality and rigor.\nYou have excellent interpersonal skills, the ability to work independently as well as part of a team, including cross-functional collaboration with product and design.\nYou have a Master's Degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience.

PhD in Computer Science, Machine Learning, Information Retrieval, or a related field, or equivalent research experience demonstrated through publications, patents, or significant open-source contributions.\nTrack record of publishing or presenting at top-tier research venues such as NeurIPS, ICML, ACL, SIGIR, WWW, or equivalent.\nExperience applying LLMs and generative AI techniques to production search or recommendation systems.