Develop deep learning models and applications based on the product features Submit high-quality experimental research codes and collaborate with colleagues in an efficient manner Run machine learning tests and experiments with records; perform statistical analyses and fine-tuning with review and team discussion Participate in the planning and design of data collection and annotation schemes, and collaborate with the data team for internal data management Submit production and deployment code to optimize model performance and inference efficiency Draft technical patents and publish papers in relevant academic conferences and journals Solid understanding and practical experience in deep learning to solve CV problems (e.g., image classification, object detection, semantic segmentation, instance segmentation) Proficiency in machine learning and deep learning libraries such as Scikit-learn, Tensorflow, Pytorch, etc. Familiarity with common CV deep learning networks, such as Resnet, EfficientNet, Yolo, Mask-RCNN, RetinaNet, DeepLab and other models Be detail-oriented, well-organized, self-motivated, and constantly striving to learn, explore, and be challenged; Be a team player with good communication skills Master of Science degree or higher in Computer Science, Statistics, or related field Experience in medical image processing (e.g., AI projects for CT, MRI, fundus or pathology photos) Two or more years of experience in real-world ground-up projects Participated/won/ranked high in CV or machine learning competitions (e.g. MICCAI Grant Challenge, Kaggle Challenges) Published papers in reputable academic conferences or journals