Localization and Mapping Engineer

Job Description Roles & Responsibilities 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: Fuse LiDAR point clouds and camera images. Generate BEV or top-view map representations. Detect and extract lanes, road boundaries, junctions, landmarks, traffic signs, traffic lights, and other map elements. 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. Desired Candidate Profile Bachelor's degree in Computer Science, Electrical Engineering, Robotics, or a related field (Master's is a plus). 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: LiDAR, Cameras, GNSS/INS, IMU, 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. Company Industry IT - Software Services Department / Functional Area Engineering Keywords Localization And Mapping Engineer Get real-time job updates only on our App