Voxelmaps

Voxelmaps is seeking a Senior Mapping Localization Engineer to work in Bristol, UK. The role primarily involves enhancing the core algorithms for mapping and localization, improving consistency and accuracy across mapping datasets. The ideal candidate will have a strong background in robotic systems, computer vision, and geospatial technologies. Benefits include a salary of £85,000 to £105,000 per year and a flexible work model that accommodates remote, hybrid, or office-based settings.

Voxelmaps is seeking a Senior Mapping & Localization Engineer in Oxford, UK, to enhance geospatial systems. This role focuses on improving map consistency across datasets using advanced techniques in SLAM, point cloud registration, and sensor fusion. The ideal candidate will possess strong C++ and Python skills, with at least 5 years of relevant experience. The position offers a hybrid work model with a competitive salary between £85,000 and £105,000 per year.

Voxelmaps Careers Thank you for exploring our Career Page. We invite you to explore our current job openings and apply for the position that best aligns with your qualifications. Alternatively, you can submit a general application using the link provided below. Your future with us begins here! Current Openings Position: Senior Mapping Localization Engineer Location: Bristol, United Kingdom Job Id: 423 # of Openings: 1 Voxelmaps builds spatial intelligence systems that transform large-scale sensor data into accurate, usable geospatial and mapping products. We are seeking a Senior Mapping & Localization Engineer to enhance and develop core algorithms and infrastructure for localization, sensor alignment, camera geometry, and spatial data integration workflows in Bristol, UK. This role focuses on improving map consistency and accuracy across large capture datasets by solving problems in SLAM loop closure, point cloud registration, sensor fusion, camera calibration, and coordinate system transformations. You will work at the intersection of robotics, photogrammetry, computer vision, and geospatial systems. Responsibilities Mapping & Localization Enhance existing or design and implement new SLAM pipelines to improve global trajectory consistency and reduce drift across large-scale mapping datasets collected from different sources. Improve localization robustness across multi-session and multi-sensor capture systems. Point Cloud Registration & Alignment Improve point cloud registration pipelines for LiDAR and multi-modal datasets. Develop workflows for cross-session map alignment and map merging. Sensor Fusion & Spatial Integration Improve and develop sensor fusion pipelines integrating LiDAR, cameras, GNSS, IMU, odometry, and external references. Implement temporal synchronization and uncertainty-aware fusion methods. Integrate third-party spatial datasets and external map sources into Voxelmaps mapping pipelines. Coordinate Systems & Transformations Design and maintain coordinate transformation frameworks across different coordinate systems. Implement georeferencing and transformation validation workflows. Support spatial accuracy assessment and error analysis. Camera Systems & Optimization Maintain and optimize camera calibration and camera pose optimization pipelines. Improve trajectory accuracy through joint optimization of sensor geometry and pose estimates. Required Qualifications & Skills BS / MS in Computer Science, Robotics, Geomatics, Photogrammetry, Mathematics, or related field. 5+ years of experience in robotics, mapping, computer vision, or geospatial systems. Strong background in SLAM, localization, sensor fusion, geometric optimization, and 3D reconstruction. Strong C++ and Python skills. Experience with numerical optimization and geometry libraries. Technical Experience SLAM / Optimization GTSAM Ceres Solver g2o Factor graph optimization Bundle adjustment Point Clouds / Registration PCL Open3D ICP / NDT registration LiDAR processing Computer Vision OpenCV Camera models Calibration methods Pose estimation SfM / photogrammetry Robotics ROS / ROS2 Sensor synchronization GNSS / IMU integration State estimation Geospatial Coordinate reference systems Geodesy PROJ GDAL Transformation pipelines Preferred Qualifications Experience with large-scale mapping systems or mobile mapping platforms. Experience processing city-scale LiDAR datasets. Understanding of uncertainty propagation and covariance modeling. Experience with multi-session localization and map fusion. Background in photogrammetry or visual-inertial systems. Publications or open-source contributions in SLAM, mapping, or calibration. Work Environment This role may be remote, hybrid, or office-based depending on the selected candidate location. Candidates must be comfortable collaborating across global teams and time zones. Job Details Timeline: For immediate hire Job Type: Full Time / Permanent, with benefits required by law Location: Bristol, UK Work Model: Remote, hybrid or office-based depending on selected candidate location Pay: £85,000 - £105,000 per year, depending on experience EEO Statement: Voxelmaps is an equal opportunity employer. All qualified applicants will receive consideration for employment. Supplier shall not discriminate against any worker based on age, disability, ethnicity, gender, marital status, national origin, political affiliation, color, race, religion, sexual orientation, gender identity or expression, union membership, protected veteran status, genetic information, or any other status protected by country law, in hiring and other employment practices. Supplier shall not require pregnancy or medical tests, except where required by applicable laws or regulations or prudent for workplace safety, and shall not improperly discriminate based on test results. Pay Range: £85,000 - £105,000 per year

Senior Mapping Localization Engineer - Oxford, United Kingdom (Hybrid) Job Id: 422 # of Openings: 1 Voxelmaps builds spatial intelligence systems that transform large-scale sensor data into accurate, usable geospatial and mapping products. We are seeking a Senior Mapping & Localization Engineer to enhance and develop core algorithms and infrastructure for localization, sensor alignment, camera geometry, and spatial data integration workflows in Oxford, UK. This role focuses on improving map consistency and accuracy across large capture datasets by solving problems in SLAM loop closure, point cloud registration, sensor fusion, camera calibration, and coordinate system transformations. You will work at the intersection of robotics, photogrammetry, computer vision, and geospatial systems. Responsibilities Mapping & Localization Enhance existing or design and implement new SLAM pipelines to improve global trajectory consistency and reduce drift across large-scale mapping datasets collected from different sources. Improve localization robustness across multi-session and multi-sensor capture systems. Point Cloud Registration & Alignment Improve point cloud registration pipelines for LiDAR and multi-modal datasets. Develop workflows for cross-session map alignment and map merging. Sensor Fusion & Spatial Integration Improve and develop sensor fusion pipelines integrating LiDAR, cameras, GNSS, IMU, odometry, and external references. Implement temporal synchronization and uncertainty-aware fusion methods. Integrate third-party spatial datasets and external map sources into Voxelmaps mapping pipelines. Coordinate Systems & Transformations Design and maintain coordinate transformation frameworks across different coordinate systems. Implement georeferencing and transformation validation workflows. Support spatial accuracy assessment and error analysis. Camera Systems & Optimization Maintain and optimize camera calibration and camera pose optimization pipelines. Improve trajectory accuracy through joint optimization of sensor geometry and pose estimates. Qualifications BS / MS in Computer Science, Robotics, Geomatics, Photogrammetry, Mathematics, or related field. 5+ years of experience in robotics, mapping, computer vision, or geospatial systems. Strong background in SLAM, localization, sensor fusion, geometric optimization, and 3D reconstruction. Strong C++ and Python skills. Experience with numerical optimization and geometry libraries. Technical Experience SLAM / Optimization GTSAM Ceres Solver g2o Factor graph optimization Bundle adjustment Point Clouds / Registration PCL Open3D ICP / NDT registration LiDAR processing Computer Vision OpenCV Camera models Calibration methods Pose estimation SfM / photogrammetry Robotics ROS / ROS2 Sensor synchronization GNSS / IMU integration State estimation Geospatial Coordinate reference systems Geodesy PROJ GDAL Transformation pipelines Preferred Qualifications Experience with large-scale mapping systems or mobile mapping platforms. Experience processing city-scale LiDAR datasets. Understanding of uncertainty propagation and covariance modeling. Experience with multi-session localization and map fusion. Background in photogrammetry or visual-inertial systems. Publications or open-source contributions in SLAM, mapping, or calibration. Work Environment This role may be remote, hybrid, or office-based depending on the selected candidate location. Candidates must be comfortable collaborating across global teams and time zones. Job Details Timeline: Immediate hire Job Type: Full Time / Permanent (benefits required by law) Location: Oxford, UK Work Model: Remote, Hybrid, or Office-based depending on location Pay: £85,000 - £105,000 per year (based on experience) EEO Statement Voxelmaps is an equal opportunity employer. All qualified applicants will receive consideration for employment. Supplier shall not discriminate against any worker based on age, disability, ethnicity, gender, marital status, national origin, political affiliation, color, race, religion, sexual orientation, gender identity or expression, union membership, protected veteran status, genetic information, or any other status protected by country law, in hiring and other employment practices. Supplier shall not require pregnancy or medical tests, except where required by applicable laws or regulations or prudent for workplace safety, and shall not improperly discriminate based on test results.