Apple-posted 3 months ago
CA
5,001-10,000 employees

Apple Maps is seeking a passionate AI engineer to help shape the future of geospatial intelligence and revolutionize how the changing world is understood, modeled and experienced. As part of our Basemap data team, you will lead the development of Agentic AI systems leveraging generative AI technologies to create next-generation mapping solutions, solving challenging, real-world problems with massive global impact. You will also provide direction, guidance and mentorship to ML engineers throughout the wider Maps organization. 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!

  • Work on challenging problems at the intersection of deep learning, computer vision, LLM, Foundation Models, Agentic AI, and geospatial data.
  • Collaborate with a cross-functional team of engineers, ML scientists, and map specialists to build AI systems that understand the world at scale.
  • BS with at least 5 years of machine learning or software engineering experience, with recent work on LLMs or generative AI.
  • Strong software engineering fundamentals with experience in shipping production systems.
  • Experience with LLMs, VLMs, or other Generative AI systems at scale.
  • Strong experience with ML and backend stack.
  • Hands-on experience with processing large datasets and training ML models in distributed environments.
  • Strong experience with Python, PyTorch, TensorFlow, JAX, containerization, FastAPI, REST/GraphQL, Java, and Scala.
  • Strong interpersonal collaboration and communication skills, ability to work with high ambiguity and minimum supervision.
  • MS or PhD in Computer Science, Artificial Intelligence, Machine Learning or related field with 10+ years of experience shipping ML models at scale.
  • Experience working with spatial/geospatial data.
  • Contributions to open-source ML/LLM tools or libraries.
  • Experience with frameworks like LangGraph, MCP, or LlamaIndex.
  • Understanding of retrieval-augmented generation (RAG), RLHF, multi-agent workflows, or LLMOps.
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