•Develop, design, and optimize deep learning networks for 3D/4D object /OCC recognition

•Conduct research on key issues in multi-task learning, including loss balancing and task conflict resolution, to improve network stability and generalization capability

•Research and develop tracking algorithms for dynamic objects (e.g., pedestrians, vehicles, animals) and continuously optimize their performance

•Collaborate with global team members to deploy and optimize deep learning networks on defined SoCs; possess in-depth understanding of post-training quantization, quantization-aware training (QAT), and pruning techniques

•Perform data mining, scene classification, and preprocessing for autonomous driving object detection tasks; participate in dataset construction, annotation, and training sample management to enhance algorithm performance.

•Master’s degree or above in Computer Science, Automation, or related fields

•>5 years working experience in computer vision for meachine learning in low computation power SOC

•Knowledgeable in common perception algorithms (e.g., Transformer, BEV, OCC, etc.

•Proficient in Python and C++ with strong programming skills; familiar with common machine learning/deep learning frameworks such as TensorFlow and PyTorch

•Experience in monocular vision projects is preferred, such as 3D perception based on monocular images, SLAM, or temporal modeling

•Familiar with model optimization and acceleration techniques; experience in model deployment is a plus

•Understanding of model quantization(PTQ,QAT), capability of tuning the model structure to fulfil the specific SoC runtime requirement

•Understanding of multi-task learning principles and related techniques (e.g., parameter sharing, soft/hard task assignment, dynamic weight adjustment)