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
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.
