CV/Multimodal/Generative AI Algorithm Engineer, TikTok Global E-Commerce
2 months ago
About the team
Relying on TikTok's global e-commerce scene, the team is committed to optimizing TikTok’s global e-commerce logistics fulfillment services through technologies such as machine learning, deep learning, and operational research optimization. Externally, it provides cheap, fast, high-quality logistics services that meet different needs for global consumers and merchants; internally, it builds an intelligent logistics system to save costs, save time, improve efficiency, and improve stability for the fulfillment of orders. Responsibilities-Responsible for MultiModal Content understanding of TikTok short videos, identifying various structured information related to e-commerce to meet the purchasing demands of different consumers.-Responsible for applying technologies such as AIGC in the field of content commerce, empowering various aspects of e-commerce such as product creativity generation, generation and optimization of product materials, virtual try-on, e-commerce video generation and editing.-Responsible for recognizing the basic attributes of products in the e-commerce scenario, improving the compliance and delivery efficiency of products, and enhancing the user's purchase experience.-Responsible for exploring cutting-edge technologies such as computer vision, AIGC (Artificial Intelligence Generated Content), and multimodal machine learning in the field of content commerce.Minimum Qualifications-Background in Software Development, Computer Science, Computer Engineering, or a related technical discipline.-Have in-depth research in a certain field of MultiModal Machine Learning, Computer Vision, and NLP, including but not limited to:a) image/video classification, detection, segmentation, action recognition, MultiModal Machine Learning pre-training, text classification, unsupervised and self-supervised learning, etc.b) multimodal generation, such as text-to-image/video/3D generation and editing, and other related multimodal experiences, diffusion model, GAN, transformer for generation tasks.-Familiar with the training and deployment of one or more frameworks such as PyTorch/TensorFlow//MXNet, and familiar with training acceleration methods such as mixed-precision training and distributed training. Preferred Qualifications-Familiar with the latest research and technological progress in model compression acceleration, including but not limited to model quantization, pruning, knowledge distillation, and inference frameworks such as TensorRT;-Strong practical skills, priority given to those who have published papers in related competitions and top academic conferences.
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