Design and own geometric map-generation algorithms that process large-scale lidar datasets into automated 3D maps for autonomous vehicles. Responsibilities include sensor optimization, pose-graph optimization, multimodal alignment, pipeline scalability, failure-mode analysis, algorithmic debugging, data-quality improvement, and integration with neighboring teams. The role requires strong C++ development, 3D geometry, point-cloud processing, linear algebra, numerical optimization, and probability expertise.

Design and own geometric map-generation algorithms processing large-scale lidar data into automated 3D maps. Improve distributed map-building pipeline runtime, scalability, and data quality; diagnose failure modes and deliver measurable algorithmic fixes. Collaborate on data contracts and integrations while applying geometric optimization, spatial transforms, point-cloud processing, and mapping techniques for autonomous vehicles.

Design, develop, and maintain scalable backend infrastructure and data-processing pipelines for Aurora’s static and dynamic mapping systems. Build reliable services for storing and serving map data, improve testing and performance, and collaborate with internal and external customers on system designs and features. The role focuses on distributed systems, data versioning, cloud-native technologies, and high-throughput mapping infrastructure supporting autonomous vehicles.