Apple Maps is seeking a passionate AI research scientist to help shape the future of geospatial intelligence and revolutionize how the changing world is understood, modeled and experienced. As part of our AI research team, you will research and apply cutting-edge deep learning and generative AI technologies to create next-generation mapping solutions, solving challenging, real-world problems with massive global impact. This role is pivotal in advancing Apple's geospatial capabilities, enabling smarter, more responsive mapping systems that can adapt to the dynamic nature of the world!In this role, you'll work on some of the most challenging problems at the intersection of deep learning, computer vision, LLM, Foundation Models, Agentic AI, and geospatial data. You'll collaborate with a cross-functional team of engineers, ML scientists, and map specialists to build AI systems that understand the world at scale. You Will: * Lead and contribute to applied research projects that push the boundaries of AI applied to geospatial intelligence. * Design and develop advanced machine learning and deep learning models to understand large scale complex geospatial data and perform geospatial reasoning. * Experiment with intelligence systems capable of interpreting and updating maps autonomously. * Collaborate with engineering and map specialists to translate prototypes into scalable systems with highly user-centric UI implementations. * Stay on top of the latest advancements in AI/ML and contribute to our technical vision and strategy. * Advocate for responsible AI practices ensuring ethical solutions in alignment PhD in computer science, Artificial Intelligence, Machine Learning or related field. Track record of patents, or publications in top AI/ML or GIS conferences or journals. Hands-on experience with processing large datasets, training ML models in distributed environments, and deploying large-scale models in production. Familiarity with emerging technologies such as Mixture of Experts (MoE) architectures, AI agents, and Retrieval-Augmented Generation (RAG). Understanding of post-training strategies like RLHF, DPO, or equivalent approaches. Array