TAYEE.AI · Careers

SLAM Algorithm Engineer

Develop and optimize multi-sensor fusion SLAM systems (LiDAR/vision/IMU/wheel odometry) for high-precision, robust localization and mapping.

Responsibilities

Develop and optimize multi-sensor fusion SLAM systems for high-precision, high-robustness localization and mapping
Research and solve real-world SLAM challenges: loop closure, relocalization, dynamic environments, large-scale map management
Own SLAM frontend feature extraction and matching (visual features, line/plane features, point cloud registration such as ICP, NDT) and backend nonlinear optimization (g2o, Ceres, GTSAM)
Complete calibration and spatio-temporal synchronization of cameras, LiDAR and IMU
Port, accelerate and deploy SLAM algorithms on embedded platforms (ARM, DSP, GPU, NPU)
Track frontier algorithms (NeRF-SLAM, semantic SLAM, learning-based features/loops) and drive product adoption

BSc or above in Computer Science, Automation, Robotics, Surveying, Mathematics or related; PhD preferred
Solid math foundation: linear algebra, probability, Lie groups/algebras, numerical optimization, multi-view geometry, Kalman/particle filters
1+ year hands-on SLAM project experience; familiar with at least one mainstream framework (ORB-SLAM, VINS, LOAM/LIO-SAM, Cartographer, MSCKF)
Proficient in C++ and Python; good engineering practices (CMake, Git, GTest)
Skilled with OpenCV, Eigen, Ceres/g2o/GTSAM, PCL, ROS/ROS2
Plus: AD, robotics competitions, AR/VR, AGV/AMR delivery experience; CUDA/OpenCL/NEON acceleration; publications at IROS/ICRA/ICCV/CVPR; deep learning for features, loop closure or pose regression