Localization and Mapping Engineer

Purpose of the Job: Develop and validate localization, SLAM, and HD-mapping solutions for L3/L4 autonomous vehicles using LiDAR, cameras, GNSS/INS, IMU, and vehicle odometry. The role also includes developing automated LiDAR-camera fusion pipelines to generate bird’s eye-view (BEV) maps and manually labeling and correcting HD-map elements. Job Responsibilities & Duties: • Develop and test LiDAR, visual, inertial, and multi-sensor SLAM and localization algorithms. • Fuse LiDAR, camera, GNSS/INS, IMU, and wheel-odometry data. • Implement scan matching, map matching, loop closure, drift correction, pose initialization, and re localization. • Develop automated pipelines that: o Fuse LiDAR point clouds and camera images. o Generate BEV or top-view map representations. o Detect and extract lanes, road boundaries, junctions, landmarks, traffic signs, traffic lights, and other map elements. o Support automatic map creation and labeling using AI and perception models. • Manually label, review, and correct HD-map geometry, topology, lanes, junctions, landmarks, and traffic elements. • Create, georeference, validate, update, and version LiDAR, camera, and HD maps. • Convert and work with OpenDRIVE, Lanelet2, OSM, GeoJSON, Shapefile, KML/KMZ, GPX, PCD, and LAS/LAZ data. • Apply WGS84, UTM, ENU/NED, EPSG, and other coordinate-system transformations. • Perform sensor calibration, frame alignment, and time synchronization. • Evaluate localization and map-quality KPIs, including accuracy, drift, availability, robustness, and re-localization performance. • Optimize algorithms for real-time execution and integration with the autonomous-driving stack. • Support field data collection, vehicle testing, debugging, and documentation.Education: Bachelor’s degree in Computer Science, Electrical Engineering, Robotics, or a related field (Master’s is a plus). Experience: • 0–2 years of relevant experience in autonomous driving, robotics, localization, SLAM, mapping, AI, or perception. • Academic projects, internships, and personal projects may be considered relevant experience. • Understanding of localization, SLAM, mapping, and sensor fusion. • Experience with one or more of the following sensors: o LiDAR o Cameras o GNSS/INS o IMU o Wheel odometry • Knowledge of coordinate frames, georeferencing, map projections, and local/global transformations. • Understanding of AI and perception concepts such as object detection, segmentation, feature extraction, sensor fusion, and model evaluation. • Experience with C++ or Python. • Familiarity with ROS 2, Linux, Git, CMake, and recorded sensor data such as rosbag or MCAP. • Ability to analyze sensor data, debug problems, and document results. Preferred Skills • Experience with LiDAR-camera fusion and BEV perception. • Experience with EKF/ESKF, factor graphs, or pose-graph optimization. • Knowledge of ICP, GICP, NDT, LiDAR odometry, visual odometry, or visual-inertial odometry. • Experience with OpenDRIVE, Lanelet2, OSM, HD maps, or point-cloud maps. •Experience manually labeling or correcting HD-map elements. • Familiarity with PCL, Open3D, OpenCV, Eigen, Ceres, or GTSAM. • Familiarity with QGIS, GDAL, or PROJ. • Experience with RTK GNSS/INS or localization during GNSS outages. • Experience with AI frameworks such as PyTorch, TensorFlow, or ONNX Runtime. • Experience with sensor calibration, synchronization, field data collection, or vehicle testing. Soft Skills: • Strong problem-solving and analytical skills. • Good communication and documentation skills. • Ability to work effectively with perception, planning, control, and vehicle-integration teams. • Willingness to participate in field testing, data collection, map review, and manual labeling activities. • Ability to work in a fast-paced research and development environment.