Data Scientist

• Design, develop, and optimize data processing and retrieval pipelines for enterprise-level generative tasks and model training (Customer Service, Token Report, Web3 Domain Models) including 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 work, applying techniques such as pre-training, supervised fine-tuning (SFT), reward modelling, and reinforcement learning to retrieval and generation tasks.
• Apply NLP, CV, and multimodal methods to analyze user-generated content (classification, quality evaluation, trend detection, comment analysis).
• Advance reasoning and planning capabilities of foundation models using System 2 thinking and advanced decoding strategies such as MCTS and A*.
• Design and implement robust evaluation methodologies and teach models to interact with external tools, APIs, and code interpreters.
• Build agents and multi-agent systems capable of addressing sophisticated real-world problems.

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