LLM Applied Data Scientist (RAG/ NLP)

Responsibilities

• Design, develop, and optimize data processing and retrieval pipelines for enterprise-level generative tasks and mode training applications (Customer Service, Token Report, Web3 Domain Models). This includes embedding, reranking, context engineering, and query rewriting models.
• Research and evaluate advanced AI-native retrieval algorithms (e.g., low-latency, multimodal retrieval, hierarchical retrieval, GraphRAG) to strengthen large-scale LLM/VLM/Agentic AI capabilities in Binance products.
• Collaborate with infrastructure and application teams to integrate RAG pipelines into production systems, ensuring scalability, reliability, and measurable business impact.
• Develop and optimize retrieval and ranking pipelines (indexing, vector search, retrieval scoring, reranking) to improve user experience.
• Participate in LLM training and RAG system, staying current with techniques such as pre-training, SFT, and reinforcement learning, and apply them to retrieval and generation tasks.
• Apply NLP, CV, and multimodal methods to analyze user-generated content (classification, quality evaluation, trend detection, comment analysis).

Requirement