Online Mapping Engineer

We are seeking an Online Mapping Engineer to join our team. You will work on building the online mapping system that turns raw data from multiple sensors into an accurate, continuously updated 3D model of the driving environment. This model captures lanes, road boundaries, crosswalks, intersection layouts, and traffic control information to support decisions across the autonomous driving system. You will write models, write C++, and ship both into vehicles on the road, running on NVIDIA DRIVE Orin and Thor. Responsibilities: Design, train, and improve PyTorch models that predict online map elements (lanes, road boundaries, crosswalks, road markings, topology) from camera, LiDAR, and/or radar, using BEV and vectorized-map representations. Solve the hardest scenes: model intersection topology (connectivity between incoming and outgoing lanes, turn paths, merges and splits) and traffic light understanding (detection, state, and association of each light to the lanes it controls). Write production C++ for geometry-based post-processing: curve and surface fitting, association, multi-frame fusion and tracking, topology reasoning, and consistency checks on DNN outputs. Apply multi-view geometry, calibration, coordinate transforms, and sensor fusion to produce 3D maps that stay accurate and stable over time. Optimize and deploy models alongside algorithms on NVIDIA DRIVE Orin and Thor under real-time and safety constraints, and get them running in vehicles. Lead the narrative around GT and the data loop by planning datasets and defining GT and labeling requirements. Work with labeling, data, and tooling partners to produce high-quality GT. Mine data, choose metrics, and analyze failure cases to drive improvements. Define critical metrics and build evaluation tooling to measure quality gains against large-scale real-world data. Partner with perception, localization, planning, and infrastructure teams to integrate online mapping into the full AV stack, across L2++, L3, and L4 programs. Contribute to technical direction and architecture decisions across the learned and geometric components of the mapping system. Requirements: MS or PhD or equivalent experience in Computer Science, Electrical Engineering, Robotics, or a related field, or equivalent experience 5+ years of industry or research experience in autonomous driving, robotics, or computer vision. Strong foundation in 3D geometry: camera models, projective geometry, coordinate transforms, optimization, and curve/surface fitting. Strong C++ skills, with a track record of designing and shipping production-quality algorithm code that is fast, reliable, and maintainable. Hands-on experience training deep neural networks in PyTorch, including model design, training pipelines, debugging, and performance analysis. Experience with data-driven development: building datasets, defining labels and GT, choosing metrics, and improving models by analyzing failures. Excellent problem-solving and communication skills, and the ability to drive technical direction across teams. Nice to Have: Direct experience with DNN-based online or HD map generation, or BEV perception (e.g., vectorized map prediction, lane topology learning), or equivalent experience to BEV perception. Experience with intersection understanding, such as lane connectivity and topology reasoning in sophisticated junctions, or traffic light detection and lane association. Experience combining learned models with geometric post-processing in a production AV system and publications or open-source contributions in 3D vision, mapping, or autonomous driving. Background in sensor fusion, multi-object tracking, or temporal fusion for map elements. Experience deploying DNNs and algorithms on embedded GPUs (CUDA, TensorRT), ideally on DRIVE Orin or Thor, under real-time constraints. Salary: The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5. Benefits: You will