Algorithm Engineer

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 large-scale multi-modal (vision, speech, natural language, etc.) algorithm research and implementation in e-commerce scenarios.
• Responsible for pre-training and tuning multi-modal models using massive e-commerce data.
• Participate in the research and development of methods to use multi-modal information more efficiently, dig in-depth the valuable information contained in each modal in the e-commerce scenario, and fully understand it in multiple dimensions.
• Support the production of scalable and optimized 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.

Requirements

• Minimum 3 years of work experience in related fields.
• Familiar with one or more deep learning frameworks such as TensorFlow, Pytorch;
• Have a deep understanding of multimodal mainstream research directions (multimodal representation learning, modal transformation, alignment, multimodal fusion, co-learning).
• Have deep understanding and project experience in NLP, entity mining, text classification, detection, segmentation, video classification, etc.
• Candidates who have practical experience in DALL-E, CLIP, GLIDE and other related projects are preferred.
• Candidates who have good performance in international competitions in related fields ( such as VQA, Image Captioning/Retrieval) are preferred.
• Candidates who have published papers in high-level computer science conferences (ACL, EMNLP, NIPS, AAAI, etc.) are preferred.