Job Description

Apple Maps enables millions of delightful user experiences every day across the world. Places data is where the Maps experience truly comes to life, whether someone is trying to discover a unique restaurant, a weekend getaway, or an essential service. Our mission is simple — to build the freshest, most accurate Places data, and powering features that enhance daily life.

With us, you will help create the best global Places dataset, which powers Maps and location experiences across the Apple ecosystem. Our team ingests, ranks, and conflates hundreds of sources of places information — including photos, reviews, and much more — and augments them using cutting-edge machine learning techniques at scale. We are a high-powered, geographically distributed team with years of geospatial knowledge, systems expertise, and product development experience.

Passionate about Generative AI, Computer Vision, Spatial AI and other emerging technologies? This could be the perfect opportunity for you!

Maps are a reflection of the physical world, and capturing this reality in a digital form is both highly challenging and rewarding. As the world constantly evolves, keeping Maps up to date demands significant investments in Machine Learning and Engineering. From Computer Vision to Generative AI and classic software engineering, maps data leverages cutting-edge ML techniques to process, interpret, and contextualize information from unstructured data ranging from text to 3D imagery.

As the Director of Places Data and ML team, you will lead all aspects of engineering for Places Data. You will combine AI/ML techniques with software engineering to solve complex map data challenges. You will collaborate with world-class cross-functional teams, including engineers, data analysts, labelers, and validators, to develop innovative solutions.

You will also play a key role in architecting a sophisticated ML-centric data platform, collaborating with some of the brightest engineering minds in the world. Your work will span a wide range of models, from large-scale data center grade solutions to lightweight edge solutions. By ensuring the freshest and most accurate data, your contributions will power seamless and delightful user experiences.

Successful leaders at Apple balance both technical execution and the long-term vision for their team. They collaborate effectively with partner and product teams to define goals, set expectations, and shape roadmaps. As a leader, you will provide both technical and career mentorship, empowering your team to succeed. Strong interpersonal skills are essential for communicating technical complexities clearly to engineering partners, product management teams, and executives across Apple.

Preferred Qualifications

Experience in Maps Data, specifically Business Listings and other Places of Interest.
PhD or Master's degree in AI, Machine Learning, Computer Science, Mathematics, or a related field.
Experience in evaluating and deploying large-scale machine learning models, deep learning, and natural language processing solutions.
A track record of publishing research papers in leading AI/ML journals is a plus.

Minimum Qualifications

15+ years of experience applying and leading engineering teams that apply sophisticated statistical and ML methods to analyze and process large, complex data sets.
Experience in one or more of the following domains: Algorithms, Architecture, Artificial Intelligence, Data Mining, Deep Neural Networks, Distributed Systems, Information Retrieval and Machine Learning.
Experience in Computer Vision, Natural Language processing and Unstructured Web Data.
Strong foundational understanding of mathematics behind modern machine learning, including linear algebra and statistics.
Familiarity with databases, SQL, and scripting languages.
Proven ability to translate requirements from design to implementation, both independently and within larger teams.
Demonstrated ability to build and inspire teams to achieve extraordinary results.
Master's degree or equivalent experience in AI, Machine Learning, Mathematics, Computer Science, or a related field.