About the Role
We are seeking a Research Data Scientist who combines rigorous research thinking with a drive to see findings land in production. You will conduct original research in generative AI and LLMs applied to crypto-native problems — spanning reasoning under uncertainty, agent reliability in adversarial financial environments, and multi-modal market intelligence.
This is a research-first role at the intersection of three critical frontiers: post-training alignment and reasoning for financial agents, efficient inference and test-time scaling, and multi-agent coordination in open, adversarial environments. The goal is real-world impact — not just papers, but agents that trade, analyze, and act for millions of users.
You formulate research questions, design rigorous experiments, build evaluation frameworks, and collaborate with engineers to bring research into production.
Responsibilities
Conduct original research in generative AI and large language models applied to crypto and financial domains — with focus areas including reasoning under market uncertainty, alignment of trading agents, multi-agent coordination, and test-time scaling for time-critical decisions
Design and execute rigorous experiments — formulating clear hypotheses, implementing model and training variants including RLVR-based reasoning approaches, running systematic ablations, and drawing statistically sound conclusions
Develop novel post-training methodologies and evaluation frameworks for LLMs operating in crypto contexts — covering chain-of-thought quality for market analysis, agent decision consistency, and robustness against adversarial prompt injection
Research test-time scaling techniques — process reward models, self-consistency, Monte Carlo Tree Search-based planning — and apply them to improve agent reasoning quality in ambiguous, fast-moving market conditions
Critically track developments across the research community at NeurIPS, ICML, ICLR, ACL, and crypto-adjacent venues — identifying high-leverage opportunities to apply state-of-the-art techniques to Binance's unique challenges
Qualifications
Master's or PhD in Machine Learning, Computer Science, Mathematics, Statistics, or related field strongly preferred; exceptional Bachelor's candidates with demonstrable research output will be considered
0–5 years of research or industry experience in ML/AI; strong academic lab or research internship experience equally valued
Deep understanding of transformer architectures, large language model pretraining dynamics, and post-training methodology — including the shift from RLHF toward RLVR-based reasoning model training
Proficiency in Python and PyTorch, Critically, you work in an AI-native way — using vibe coding practices with tools like Claude Code, Cursor, or Copilot Workspace as your primary development workflow
Rigorous mathematical foundations: linear algebra, probability theory, information theory, stochastic processes, and numerical optimization
Bilingual English/Mandarin is required to be able to coordinate with overseas partners and stakeholders
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