30 days ago
Sŏngnam, Korea, SouthMid Level
Responsibilities
- Develop offline SLAM and localization algorithms using camera, LiDAR, IMU, and GNSS data.
- Develop trajectory estimation, pose optimization, and global alignment algorithms for large-scale driving data.
- Develop multi-sensor localization, self-calibration, and calibration algorithms.
- Improve 3D reconstruction accuracy using multi-view geometry, structure-from-motion, and bundle adjustment.
- Research and apply NeRF and 3D Gaussian Splatting techniques to data pipelines.
- Build and improve quality evaluation and validation systems for localization, calibration, and 3D geometry data.
- Apply algorithms to large-scale data-processing pipelines and establish automated data-generation and quality-improvement processes.
- Collaborate with Perception, AI, and Data Platform organizations to improve autonomous-driving AI training data quality.
Requirements
- Master's degree in computer science, electrical engineering, mechanical engineering, robotics, or a related field plus at least three years of experience, or a PhD.
- Understanding of computer vision, robotics, SLAM, localization, or 3D geometry.
- Experience developing algorithms and software with C++.
- Experience using Python for data analysis and algorithm validation.
- Understanding of linear algebra, multi-view geometry, probability, and nonlinear optimization.
- Development experience in a Linux environment.
- Preferred experience developing visual SLAM, LiDAR SLAM, visual odometry, or offline localization algorithms.
- Preferred experience with multi-sensor localization, sensor fusion, sensor self-calibration, and camera/LiDAR/IMU calibration.
- Preferred experience with factor graph optimization, pose graph optimization, bundle adjustment, structure-from-motion, multi-view stereo, point-cloud registration, or dense 3D reconstruction.
- Preferred experience with NeRF, 3D Gaussian Splatting, camera- and LiDAR-based 3D geometry reconstruction, and large-scale driving-data processing.
- Experience with Ceres Solver, GTSAM, COLMAP, or Open3D is preferred.
- Publications in relevant conferences or contributions to open-source projects are preferred.
Benefits
- A three-month probationary period may apply.
- The interview process includes document screening, a video interview, and a second interview conducted in person or by video; the process may change depending on scheduling and circumstances.
- Veterans and employment-protection beneficiaries receive preference under applicable laws, as do registered individuals with disabilities.
About 42dot
42dot builds software-defined vehicle and autonomous mobility platforms for automakers and mobility service providers. Its products include AI-driven vehicle software, data-based update services, and core platform components such as API gateways and identity/access management to connect services securely. The privately held company is headquartered in Seongnam-si, Gyeonggi-do, and focuses on transportation and automotive markets.