LLM Applied Data Scientist (RAG/ NLP)
Applied data scientist at Binance building and optimizing RAG and retrieval/ranking pipelines (embeddings, reranking, query rewriting, GraphRAG) for LLM-powered products like customer service and token reports, and participating in LLM training (SFT, reinforcement learning) and NLP/multimodal content analysis. Core stack: Python/C++, PyTorch, vector databases.
- Compensation
- Not disclosed
- Location
- Hong Kong; Singapore
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- 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++
- Strong communication skills, intellectual curiosity, and passion for lifelong learning
- Solid theoretical foundation in information retrieval, NLP, and deep learning
- 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)
Nice to have
- Experience with embeddings, reranking, query understanding
- Publications in top-tier conferences/journals (NeurIPS, ICML, ACL, CVPR, SIGIR, KDD, WWW)
- Awards in ACM/ICPC or similar competitions
Full description
Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, and more. Our mission is to increase the freedom of money and financial access for people around the world.
About the Role
We are seeking a highly skilled Research Scientist/Engineer to advance the reasoning and planning capabilities of large foundation models. In this role, you will enhance model performance across the entire development lifecycle—including data acquisition, supervised fine-tuning (SFT), reward modelling, and reinforcement learning—while driving innovations in reasoning and decision-making. You will synthesise large-scale, high-quality datasets through rewriting, augmentation, and generation techniques to strengthen foundation models during pretraining, SFT, and RL stages. A key part of the role involves solving complex tasks using System 2 thinking and applying advanced decoding strategies such as MCTS and A*. You will design and implement robust evaluation methodologies, teach models to interact with external tools, APIs, and code interpreters, and build agents and multi-agent systems capable of addressing sophisticated real-world problems.
Why Binance
- Shape the future with the world’s leading blockchain ecosystem
- Collaborate with world-class talent in a user-centric global organization with a flat structure
- Tackle unique, fast-paced projects with autonomy in an innovative environment
- Thrive in a results-driven workplace with opportunities for career growth and continuous learning
- Competitive salary and company benefits
- Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)
Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.
By submitting a job application, you confirm that you have read and agree to our Candidate Privacy Notice.
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.