Target Candidates: Recent Bachelor’s or Master’s graduates in Computer Science, Automation, Applied Mathematics, Electronic Information, or related fields.

Job Responsibilities
Algorithm R&D and Implementation: Participate in the C++ implementation and optimization of in-vehicle vision algorithms (e.g., image processing, object detection, camera calibration).
3D Rendering Development: Learn and utilize OpenGL ES/Vulkan to develop in-vehicle graphics rendering modules, achieving high-quality 3D visual effects (e.g., 3D vehicle model rendering, panoramic surround view).
Engineering Support: Develop and maintain image processing toolchains based on OpenCV; assist with the cross-compilation and deployment of algorithms on embedded platforms.
Testing and Maintenance: Write unit tests, participate in code reviews, and produce high-quality technical documentation.

Requirements
Solid Fundamentals: Strong proficiency in C/C++ programming; familiarity with common data structures and algorithms.
Mathematical Proficiency: Solid foundation in linear algebra and matrix geometry; understanding of spatial coordinate system transformations.

Professional Skills
Understanding of computer graphics fundamentals; preference given to candidates with academic project experience involving OpenGL, OpenGL ES, or Vulkan.
Familiarity with OpenCV; understanding of the principles behind basic fisheye image processing algorithms.
Soft Skills: Passion for technology; strong learning ability, resilience under pressure, and a collaborative team spirit.

Responsibilities:
1. Primarily responsible for the implementation and debugging of algorithms related to path planning and control theory for autonomous robots;
2. Responsible for kinematic modeling and simulation of autonomous robots;
3. Develop optimal motion planning and control solutions for semi-enclosed environments such as industrial parks, underground parking lots, scenic areas, service areas, and campuses;
4. Continuously improve the performance of autonomous robots in complex environments;
5. Handle technical documentation, logic implementation, as well as unit and integration testing;

Requirements:
1. Bachelor’s degree or higher in Automation, Control Theory and Control Engineering, Vehicle Engineering, or related fields;
2. Familiarity with kinematic models (Ackermann, differential drive, steerable wheels) and chassis architecture/control characteristics;
3. Proficiency in development environments such as Linux and ROS2; solid programming foundation (C++);
4. Familiarity with control algorithms such as MPC, LQR, and PID;
5. Familiarity with planning algorithms such as A* and Dijkstra;
6. Experience with the Autoware.Universe open-source autonomous driving framework is preferred;
7. Strong curiosity, self-motivation, problem-solving skills, and rapid learning ability; excellent communication skills and a strong sense of teamwork.

Job Responsibilities
1. Develop visual perception algorithms based on automotive cameras;
2. Handle algorithm model development, deployment and porting to embedded platforms, quantization and acceleration, and productization.

Qualifications
1. Master’s degree or above, with R&D experience in vision-related projects;
2. Familiarity with the Linux platform; proficient in C++ and Python development with good coding practices;
3. Proficiency in deep learning model development for at least one of the following tasks: object detection, object tracking, image segmentation, or instance segmentation;
4. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and understanding of ONNX usage;
5. Understanding of the model development workflow for embedded platforms;
6. Experience in application development for automotive visual perception scenarios is preferred;
7. Diligent and responsible work attitude; strong communication and teamwork skills; proactive in analyzing and thinking through problems.

Job Responsibilities
1. Responsible for developing sensor fusion algorithms (integrating dynamic and static perception data from radar, cameras, LiDAR, etc.) in the autonomous driving field; collaborate with upstream and downstream teams to support the implementation of autonomous driving functions.
2. Responsible for the R&D and deployment of perception fusion algorithms for dynamic targets and static environmental elements—including but not limited to vehicles, pedestrians, general obstacles, lane lines, curbs, and static obstacles—while continuously improving detection capabilities for the vehicle’s surrounding environment; also responsible for forklift and cargo frame position detection.
3. Responsible for algorithm design, development, and performance iteration, ensuring the delivery quality of the algorithm software.

Job Requirements
1. Familiar with the strengths and limitations of object detection and recognition using sensors such as millimeter-wave radar, cameras, and LiDAR; capable of flexibly implementing sensor fusion.
2. Possess a solid mathematical foundation and familiarity with fundamental data fusion theories and methods, such as Kalman filtering, Bayesian estimation, data association, and track management.
3. Familiar with autonomous driving system architectures; understand the functions and requirements of modules upstream and downstream of the fusion module (e.g., perception, ego-motion estimation, planning, and control).
4. Experienced in developing detection and fusion algorithms for static environmental elements like lane lines and curbs; familiar with common association and tracking algorithms.
5. Proficient in C++ and familiar with development environments such as Linux and middleware; strong engineering skills; familiarity with CPU performance optimization and experience debugging memory-related issues are preferred.
6. Experience with autonomous driving-related functions or relevant internship experience is preferred.