Shopee

The Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed most "innocent" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do.

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.

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.