Senior/Staff Engineer, Machine Learning - Online Mapping

Benefits

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

131% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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About this role

Senior/Staff Engineer, Machine Learning - Online Mapping Mountain View, California (HQ) Who We Are Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world's most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale-empowering a safer, richer, and more connected future. About the Team Our robotics team is growing and we are looking for an ML Software Engineer to join our Online Mapping team. We are searching for an engineer with robotics and machine learning expertise to work on challenging problems in the design and implementation of onboard mapping models and algorithms, data and label management, as well as training pipelines. About the Role Building robust ML and/or mapping systems that work with real data (cameras, LiDAR, etc.) in uncertain environments, and a strong desire to contribute to the future of robot navigation for logistics and transportation. About the Work - Research, develop, and implement state-of-the-art online mapping models and algorithms. - Analyze and characterize the performance of the online mapping system, identifying opportunities for architecture, data or evaluation improvements in a E2E ML system. - Work cross functionally with other ML teams to integrate our models into centralized architectures. -

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