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!
Description
In this role, you will 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 will collaborate with a cross-functional team of engineers, ML scientists, and map specialists to build AI systems that understand the world at scale.
Minimum Qualifications
BS with at least 5 years of machine learning or software engineering, with recent work on LLMs or generative AI.
Strong software engineering fundamentals — you have shipped production systems and know how to build clean, robust, reliable and maintainable code.
Shipped LLMs, VLMs, or other Generative AI systems at scale. Strong experience with ML and backend stack.
Hands-on experience with processing large datasets, 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.
Preferred Qualifications
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.
Responsibilities
Architect large complex Agentic AI systems and implement end-to-end integrating ML components and training pipelines, inference services, tools, evaluations and safety.
Fine-tune and deploy large foundational models including LLMs, LVMs and LMMs for various real-world mapping tasks.
Contribute to and scale our AI infrastructure, making it easier to experiment and iterate quickly.
Evolve early-stage ideas into robust, production-ready systems collaborating with a large cross function team.
Eeo Content
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
