We are a startup focused on embodied intelligence applications, dedicated to combining AI, robotic perception, motion control, and real-world applications to build robotic systems capable of autonomous mobility, environment understanding, and task execution.
You will participate in the full workflow from sensor data processing and algorithm implementation to simulation validation and real-robot deployment, helping robots achieve stable and reliable autonomous mobility in complex indoor and outdoor environments. Candidates may focus on one or more directions including visual SLAM, LiDAR SLAM, multi-sensor fusion, robot navigation, and state estimation.
Responsibilities
- Participate in the R&D of robot SLAM, localization, mapping, navigation, and state estimation algorithms.
- Participate in autonomous navigation system development for bipedal, quadrupedal, or wheeled robots in indoor and outdoor scenarios.
- Process and fuse multi-source sensor data, including LiDAR, Camera, IMU, wheel odometry, depth camera, GPS / RTK, and foot-end contact information.
- Participate in development and debugging of modules such as visual SLAM, LiDAR SLAM, VIO, LIO, laser odometry, loop closure detection, and map optimization.
- Participate in features including local maps, global maps, cost maps, obstacle detection, and traversable area analysis.
- Participate in path planning and navigation module development, including global planning, local planning, trajectory tracking, obstacle avoidance, and relocalization.
- Participate in integration, deployment, testing, and performance optimization of algorithms in ROS / ROS2 systems.
- Analyze real-robot runtime data and diagnose and resolve issues such as drift, localization loss, map inconsistency, latency, and sensor anomalies.
- Read and reproduce English papers and open-source projects in SLAM, robot navigation, multi-sensor fusion, legged robot navigation, and mobile robot navigation.
- Collaborate with motion control, perception, software, and hardware teams to integrate localization and navigation capabilities into real robotic systems.
Requirements
- Major in robotics, automation, computer science, AI, surveying and mapping, electronics, control, mechanical engineering, or related fields; senior undergraduate, master's, or PhD candidates are preferred.
- Familiarity with at least one of the following areas: SLAM, state estimation, multi-sensor fusion, path planning, or robot navigation.
- Proficiency in C++ with strong capabilities in algorithm implementation, debugging, and performance optimization.
- Familiarity with Python for data processing, experiment analysis, visualization, and tooling script development.
- Familiarity with Linux development environments and proficiency with Git for code management.
- Familiarity with ROS / ROS2 fundamentals, including common messaging mechanisms, coordinate transforms, sensor topics, and robot system architecture.
- Understanding of working principles and data characteristics of common sensors, including LiDAR, Camera, IMU, Encoder, and Depth Camera.
- Strong mathematical foundation in linear algebra, 3D geometry, probabilistic estimation, optimization methods, or robot kinematics.
- Ability to read English papers and technical documentation fluently, with strong self-learning and problem decomposition capabilities.
- Strong interest in autonomous robot navigation and real-world deployment, with willingness to participate in real-robot testing and complex issue troubleshooting.
Nice to Have
- Experience with visual SLAM, LiDAR SLAM, VIO, LIO, GPS-IMU fusion, wheel odometry fusion, or state estimation for legged robots.
- Familiarity with common open-source projects or algorithm frameworks such as ORB-SLAM, VINS-Fusion, LIO-SAM, FAST-LIO, Cartographer, LOAM, RTAB-Map, and Nav2.
- Familiarity with optimization, point cloud, and visual processing tools such as GTSAM, Ceres, g2o, Eigen, Sophus, PCL, and OpenCV.
- Project experience with bipedal robots, quadrupedal robots, wheeled robots, mobile robots, or autonomous vehicles.
- Experience in legged robot navigation, including understanding of body state estimation, foot contact, terrain variation, posture change, and motion noise issues.
- Experience in mobile robot navigation, with familiarity in modules such as costmap, AMCL, global planner, local planner, trajectory tracking, and dynamic obstacle avoidance.
- Experience in multi-sensor calibration, such as camera intrinsic/extrinsic calibration, LiDAR-Camera calibration, LiDAR-IMU calibration, and time synchronization.
- Hands-on robot deployment experience, with understanding of sensor latency, timestamp synchronization, coordinate frame management, compute constraints, and real-time requirements.
- Experience in challenging scenarios such as low-texture environments, dramatic lighting changes, dynamic crowds, narrow spaces, stairs, ramps, and unstructured outdoor terrain.
- Experience in robotics competitions, research projects, open-source projects, publications, or demonstrable demos.
- Familiarity with AI coding tools and the ability to use them for paper reproduction, code development, and experiment analysis.
What You'll Gain
- Deep involvement in building autonomous navigation capabilities for embodied intelligence robots.
- Exposure to localization, mapping, and navigation challenges for bipedal, quadrupedal, and wheeled robots in real-world scenarios.
- Participation in the complete R&D workflow from algorithm development and data collection to offline evaluation and real-robot deployment.
- Collaboration with motion control, perception, software, and hardware teams to solve complex engineering challenges in real robotic systems.
- Opportunities to contribute to real-world deployments in indoor service robotics, inspection, mobile manipulation, and humanoid robot navigation.
- Top performers may receive full-time conversion or long-term collaboration opportunities.
