Qualifications: 1. Have a solid theoretical foundation in computer vision and be familiar with mainstream visual recognition algorithms such as image processing, feature extraction, target detection, recognition, positioning, tracking and measurement (for example: OpenCV, YOLO, etc.). 2. Be able to apply visual recognition technology in actual projects, conduct scheme design, model development, deployment, acceleration and optimization, and be familiar with relevant frameworks such as TensorFlow, PyTorch, etc. 3. Have practical project experience related to visual recognition, actually participate in and promote project implementation, understand the challenges and needs in industrial environments, and understand the performance requirements of related hardware. 4. Be able to analyze and solve problems independently and have good troubleshooting and debugging capabilities. 5. Have excellent teamwork spirit and communication skills, and be able to work with cross-departmental teams to promote project progress. 6. Have participated in or led the design and deployment of automotive industry visual recognition systems. 7. Be familiar with the application of deep learning in visual recognition and have relevant practical experience, such as using deep learning frameworks such as TensorFlow and PyTorch. 8. Have academic background or scientific research experience in related fields.
Minimum years of experience: 5 years Certifications if any: Familiar with OpenCV , YOLO, Halcon Most important qualification for you: Familiar with TensorFlow ,Pytorch Education, preference on major: Bachelor minimum English level expected: Read/write only
