Our Search Team focuses on LLM application algorithms, with core scenarios including AI Search and AI Bot.
We build search question-answer cards via LLM-powered generation and develop official AI assistant, leveraging LLM ChatBot, Agent, and RAG technologies to deliver accurate, real-time responses.
By closely integrating technical innovation with business scenarios, we bridge users with information and services, driving our business growth.
Responsibilities
Build AI Search Agents with planning, memory, and tool-use capabilities. Apply reinforcement learning, knowledge representation, and automated decision-making to solve complex problems end-to-end.
Enhance LLM-based query understanding: process multi-turn conversations, conduct semantic analysis on queries and documents, and ensure accurate, objective search results via LLM reasoning.
Optimize RAG and LLM pipelines: improve retrieval and ranking algorithms, train and optimize LLMs for answer generation (e.g., fine-tuning, inference optimization), and enhance the relevance and structured presentation of search results.
Translate research (e.g., Agent/LLM innovations) into production, collaborating with cross-functional teams to drive product upgrades.
Requirements
5 years of experience in Machine Learning, preferably in a fast-paced, high-growth environment.
Proficient in C++/Python; strong command of algorithms and data structures (e.g., retrieval, graph algorithms).
Basic understanding of LLM fundamentals (e.g., prompt engineering, fine-tuning workflows).
Strong problem-solving skills with a track record of delivering technical projects.
Preferred
Hands-on experience with LLM/Agent technologies: RAG optimization, RLHF/SFT for LLMs, or Agent framework development (e.g., planning/memory modules).
Excellent communication and collaboration skills, with a track record of exploring new technologies and driving technical advancements with teams.
