Senior Machine Learning Engineer - Mapping

Our Mapping organization is building national-scale, next-generation mapping systems that move beyond static HD maps toward automated, algorithmic and ML-assisted map reconstruction pipelines powered by onboard sensor data. These systems form a critical foundation for localization, perception, simulation, and autonomy at scale.

We are looking for a Senior Software Engineer to design and build the mapping algorithms and geospatial data systems behind automated map reconstruction and maintenance within our Mapping Engineering team.

In this role, you will develop the algorithms and data models that reconstruct, conflate, validate, and maintain map primitives — lanes, lane connectivity, road network graphs, boundaries, traffic controls, and signs — from large-scale multi-modal sensor data. You will own the geometric and topological correctness of the map itself: how features are represented, how disparate sources are merged, and how errors are detected before they reach the vehicle.

This is a hands-on individual contributor role with strong ownership. You will drive technical problems end to end, partner closely with Perception, Localization, and Simulation, and apply machine learning where it measurably improves accuracy, coverage, or automation rate.

• Design and implement mapping algorithms for map reconstruction and maintenance — lane and boundary extraction, road network graph construction, map conflation and matching, geometry simplification, and topology validation.
• Build and evolve geospatial data models for lane-level connectivity at intersections, road network graphs, and associated map attributes and restrictions.
• Develop large-scale distributed geospatial pipelines that process sensor-derived and third-party road data into production map releases on a recurring cadence.
• Apply machine learning and computer vision models — detection, segmentation, 3D reconstruction, BEV representations — to automate feature extraction and map change detection, and integrate them into production pipelines.
• Build automated quality, validation, and regression systems that catch geometric, topological, and semantic map defects before release, with clear accuracy metrics.
• Collaborate cross-functionally with Perception, Localization, Simulation, and Platform teams on interfaces, data contracts, and integration points.
• Diagnose and resolve system-level issues spanning geospatial data pipelines, algorithms, models, and production workflows.
• Contribute to design reviews, engineering best practices, and mentorship of engineers on the team.

• 3+ years of software engineering experience building production systems, with a substantial portion focused on mapping, geospatial, or geometric algorithms.
• Strong applied foundation in geospatial and computational geometry concepts — coordinate systems and projections, spatial indexing, geometry operations, map matching, and graph algorithms on road networks.
• Demonstrated experience designing geospatial data models and working with road network or map data structures (lanes, segments, intersections, topology).
• Hands-on experience with machine learning or computer vision workflow in production — dataset curation, model training or fine-tuning, evaluation, and deployment.
• Experience building large-scale distributed data pipelines for geospatial or sensor data.
• Proficiency in Python and C++.
• BS or MS in Computer Science, GIS, Electrical Engineering, Robotics, or a related technical field, or equivalent industry experience.
• Ability to own ambiguous, well-scoped technical problems end to end and drive them to production.

• Experience with HD maps,…