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Description
The Navigation Experience team is responsible for the end-to-end guidance experience across driving, cycling, walking, transit, and hiking. As an ML-focused engineer on our team, you will spearhead new initiatives to transform navigation into an experience that feels natural, intuitive, and tailored to a user's unique context.You’ll work deep inside our guidance system, applying machine learning to solve complex problems, making our app reason about a journey the way a knowledgeable local would. This work is highly cross-functional: you will collaborate with multiple partners across design, routing algorithms, data science, and product to deliver seamless, integrated customer experiences. We have a modern development process, analyzing requirements, building and training models, implementing and experimenting iteratively, and optimizing solutions for on-device and backend execution. We support production systems serving billions of requests daily, so occasional support outside standard business hours may be required for on-call duties and critical project needs.
Minimum Qualifications
* BS, MS, or PhD in Computer Science, Machine Learning, Mathematics, or related field, with strong industry experience building large-scale, production-grade ML applications. * Proven experience applying applied Machine Learning to solve complex, customer-facing technical problems (e.g., personalization, recommendation, contextual AI, or sequence modeling). Ability to write complex, highly-performant, clean, and maintainable solutions in C++. Solid understanding of algorithms, data structures, and software architecture. Strong problem-solving skills and comfort working with ambiguity, evolving requirements, and open-ended product questions. Good interpersonal and communication skills, possessing the ability to work both independently and collaboratively in a distributed, multi-functional team environment.
Preferred Qualifications
Experience with ML techniques applied to navigation, geospatial data, or time-series context. Familiarity with privacy-preserving approaches to ML (such as federated learning or differential privacy) and data processing. Experience with deep learning frameworks and writing Python tools for data exploration, prototyping, and pipeline development. Familiarity with navigation use cases across different modalities (driving, transit, walking, cycling, hiking). Experience with data pipelines, scalable data architectures, or cloud-native infrastructure (e.g., Spark, Kafka, Kubernetes) for model training and deployment. Familiarity with macOS or Linux development environments.
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