About the team

The team use the most advanced AI technology to combat various risks / violations in Company's e-commerce platform, maintain platform security, build a good e-commerce ecosystem, and empower business teams to improve work efficiency. We pursue the ultimate risk detection capability. The fairness and sustainability of the e-commerce ecosystem, high-quality content and merchandise are areas where we are constantly striving for improvement.

Responsibilities

• Responsible for the optimization and iteration of computer vision related models in the e-commerce scene, including fine grain classification, Product object recognition, Product subject recognition, feature extraction, logo detection, brand recognition, etc., to optimize the merchant's product loading and unloading process.
• Responsible for e-commerce short video and livestream classification, multi-modal content mining, multi-modal content understanding, optimizing e-commerce short video and livestream shopping experience.
• Responsible for e-commerce image search, photo search goods, goods duplication algorithm.
• Explore the cutting-edge technology of computer vision, responsible for the iteration and evolution of the overall algorithm and system.
• Support the production of scalable and optimised AI / machine learning (ML) models
• Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI / ML solutions from theoretical data science models.
• Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process.
• Work in a team setting and apply knowledge of statistics, scripting and programming languages required by the firm.
• Work with the relevant software platforms on which the models are deployed. Qualifications
• Work experience in a university, industry, or government lab(s), in a role with primary emphasis on AI research.
• Minimum 3 years of work experience in related fields.
• In-depth research in a certain field of multimedia and computer vision, including but not limited to : image search, image / video classification and recognition, image segmentation, object detection, OCR, graph neural networks, multimodal, unsupervised and self-supervised learning, etc.
• Familiar with the training and deployment of one or more framework models of TensorFlow / PyTorch / MXNet, and understand training acceleration methods such as hybrid precision training and distributed training.
• Familiar with the latest research and technical progress of model compression acceleration, including but not limited to model quantization, pruning, knowledge distillation, etc., and inference frameworks such as TensorRT.

Requirements

Qualifications