Binance is hiring a LLM Applied Data Scientist (RAG/ NLP) in Engineering — Taiwan, Taipei. The overview below is synthesized from the employer posting on jobs.lever.co: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
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 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.
About the Role
Day-to-day scope for the LLM Applied Data Scientist (RAG/ NLP) as described in the posting:
The listing frames the LLM Applied Data Scientist (RAG/ NLP) at Binance as a hands-on product role. 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 du
About the Role
Day-to-day scope for the LLM Applied Data Scientist (RAG/ NLP) as described in the posting:
The listing frames the LLM Applied Data Scientist (RAG/ NLP) at Binance as a hands-on product role. 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 du
Responsibilities
Day-to-day scope for the LLM Applied Data Scientist (RAG/ NLP) as described in the posting:
- 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
Experience and skills the team lists as required:
- 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. Able to identify opportunities and drive cutting-edge retrieval & RAG technologies into real-world applications.
- 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.
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.
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.