Ford China Driver Assistance Technologies is expanding engineering and research activities in the field of Automated Driving, focus to deliver world-class ADAS and AD solutions experiences for the Ford and Lincoln vehicle brands.

Master's degree or above in Computer Science, Artificial Intelligence, Information Technology, Mathematics, Electronics, Automation, Automotive Engineering, Robotics, or related fields. At least 3 years of experience in autonomous driving projects and deep learning development. Strong computer science foundation, proficient in Python programming, familiar with Linux and OpenCV, with C/C++ development experience preferred. Proficient in common coding frameworks and at least one deep learning framework such as PyTorch or TensorFlow. Deep understanding of mainstream CNN/RNN neural network structures, including but not limited to VGGNet, ResNet, LSTM, and Transformer, with relevant development experience. Familiar with current mainstream deep learning algorithms, including but not limited to BEV perception, unsupervised training, large models, object detection, image segmentation, multi-task learning, and multi-sensor fusion. Experience in one or more ML/DL/CV projects, strong problem-solving abilities, and proficient in reading technical documents in English. Excellent communication and collaboration skills, self-driven, result-oriented, and capable of deconstructing algorithm development tasks and delegation. Strong values, intrinsic motivation, excellent learning abilities, and the ability to proactively solve problems and handle work pressure. Responsible, passionate about technological innovation, and good team player with excellent communication skills. Please use Liepin to seek detailed position information. Thanks. Design and develop visual perception algorithms for driving scenarios, including vehicle/pedestrian/general object/lane/traffic signs etc. in highway and urban environments. Explore and develop 3D perception solutions, including but not limited to BEV vector perception, 3D object detection, and occupancy network perception. Develop detection and semantic segmentation algorithms based on cameras and sensor fusion. Optimize model training algorithms and streamline models to enhance accuracy and speed. Collect, process, maintain, and analyze datasets. Design evaluation metrics for algorithms and establish iterative strategies. Organize training data and develop data annotation schemes. Support perception issue analysis and fix for on-going and incoming programs