Binance - LLM Applied Data Scientist (RAG/ NLP)

• 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. • retrieval and ranking pipelines • 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. • LLM training and RAG system • Master’s in Information Retrieval, NLP, Machine Learning, Computer Vision, Multimodal Learning, or related fields. • Proficient in PyTorch with strong coding skills in Python or C++. • PyTorch • Python or C++ • Strong communication skills, intellectual curiosity, and passion for lifelong learning. Able to identify opportunities and drive cutting-edge retrieval & RAG technologies into real-world applications. • cutting-edge retrieval & RAG technologies • Solid theoretical foundation in information retrieval, NLP, and deep learning (experience with embeddings, reranking, query understanding preferred). • Hands-on experience with RAG, vector databases, multimodal/graph retrieval, or large-scale AI systems. • Strong engineering ability to translate research into scalable, production-level systems. • Self-driven, able to own projects end-to-end (design → implementation → deployment). • Publications in top-tier conferences/journals (NeurIPS, ICML, ACL, CVPR, SIGIR, KDD, WWW) are a plus; awards in ACM/ICPC or similar competitions preferred.