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• 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.

Qualifications :

• Masters 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(PTQQAT) 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)

Remote Work :

No

Employment Type :

Full-time