Lead Engineer (Traffic) - Apple Maps

Descripción del puesto:

When will rush hour start tomorrow? How long will it take for a traffic jam to clear? How is a road closure impacting millions of drivers right now? At Apple Maps, our Traffic team is dedicated to answering these questions at a global scale-powering accurate, responsive navigation experiences for millions of users every day. We're seeking a Technical Engineering Manager to lead a small, cross-functional team behind Apple Maps' real-time traffic systems. In this role you'll be hands-on-writing production-grade code, reviewing your teammates' work, and co-designing solutions that guide millions of drivers safely to their destination. You'll own the end-to-end lifecycle of traffic services, from architecture to incident response, and work closely with product teams to shape the future of Navigation. If you thrive on coaching engineers, solving complex data challenges, and building robust production services, we'd love to hear from you.
You will lead a team that builds scalable, resilient systems that process high volumes of live and historical location data to power Apple Maps' real-time traffic and routing capabilities. In this role, you will:
MS or PhD in Computer Science, Engineering, or a related field Strong experience in software development, preferably in backend or data-intensive systems and experience leading engineers (manager or tech lead role) Strong foundation in algorithms, distributed systems, and data processing at scale Availability to work overlapping hours with the Cupertino office (late Zürich time) to support cross-border collaboration
Experience in traffic, navigation, or geospatial systems Proven ability to define and deliver technical strategies aligned with product goals Strong cross-functional collaborator with data scientists, researchers, and product managers Effective communicator with the ability to influence across technical and product domains Proficiency in a typed language such as Java, Scala, or C++ Familiarity with scalable data architectures (e.g., Spark, Flink, Kafka) and cloud-native infrastructure (e.g., Kubernetes) Experience with Machine Learning a plus