Job Description

:

• Design and develop core components of the chatbot's intent recommendation and to-agent smart system, with emphasis on user behavior sequence modeling and causal inference to enhance recommendation accuracy and relevance with consideration of agent cost.
• Modeling user's CS behavior based on user profiles and historical interaction data in Shopee's e-commerce customer service ecosystem to accurately infer user's true intent.
• Collaborate with product managers, operations specialists, and backend engineers to ensure seamless integration and successful deployment chatbot recommendation systems.
• Stay up to date with advancements in recommendation technologies-including generative recommendation, retrieval-augmented generation (RAG), supervised fine-tuning (SFT) - and apply them to improve the chatbot's core capabilities.

Requirements :

• Master's degree or above in Computer Science, Artificial Intelligence, or a related discipline.
• Solid experience with the architecture and optimization of end-to-end recommendation systems, including retrieval, coarse ranking, fine ranking, and hybrid ranking stages.
• Familiarity with mainstream recommendation models (e.g., FM, DIN, DIEN, MMoE, SIM) as well as LLM-based generative recommendation models (e.g., HSTU, NoteLLM).
• Proficient in Python and experienced with deep learning frameworks such as PyTorch or TensorFlow.
• Strong analytical and problem-solving abilities, with a passion for building intelligent, user-centric products.
• Experience in training and fine-tuning large language models using techniques such as supervised fine-tuning (SFT) or reinforcement learning (RL).
• Prior experience developing or optimizing algorithms for large-scale recommendation, search, or advertising systems.
• Experience in chatbot algorithm development is a strong plus.

About the Team :
We are the Customer Service Chatbot team at Shopee Singapore, committed to developing multilingual, intelligent dialogue systems that serve a wide range of consumers and sellers. Our focus lies in applying advanced AI technologies-including recommendation systems, Large Language Models (LLMs), autonomous agents, and reinforcement learning-to customer service scenarios, continuously enhancing interaction quality and user experience.