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

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. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

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.