Content Understanding LLM Algorithm Engineer Intern (Global E-Commerce) - 2027 Start (PhD)

About the Team Through algorithm optimization and collaboration with business teams, the team conducts comprehensive quality and ecosystem governance for e-commerce products. This involves combating risks, violations, and low-quality issues, as well as constructing and optimizing the e-commerce ecosystem. The team aims to maximize platform governance effectiveness while improving operational efficiency and reducing costs. Additionally, the team is dedicated to advancing cutting-edge AI technologies to drive business transformation and development through technical innovation, covering diverse fields including but not limited to NLP, CV, multimodal models, large models, graph algorithms, and sequence algorithms.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).

Responsibilities 1. Large language Model Algorithm Development: Build domain-specific large language models (LLM/MLLM) for e-commerce, integrating domain knowledge to rapidly apply models to business scenarios. 2. E-commerce Governance Optimization: Understand e-commerce governance scenarios deeply to improve merchant/product/video/live-stream/IPR governance through algorithm optimization. Develop state-of-the-art intelligent review systems capable of “knowing why to reject” decisions. 3. Model Enhancement: Handle tasks like data construction, foundational model enhancement, instruction fine-tuning, chain-of-thought (CoT) , and parameter-efficient fine-tuning (PEFT) to achieve optimal model performance in the e-commerce domain. 4. Problem Solving for Governance Applications: Address challenges such as long text/sequence modeling, few-shot learning, content moderation, violation detection, and policy recommendation using large models and multimodal approaches. 5. Model Development and Optimization: Research and optimize e-commerce-specific NLP and multimodal large models to improve multilingual, multi-task, and multi-modal algorithm performance across various e-commerce scenarios.