Pioneer Talent Program - Research Data Scientist

Responsibilities

• Conduct original research in generative AI and large language models applied to crypto and financial domains — focus areas include 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, EMNLP, 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; 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 RLHF, RLVR-based reasoning model training, and alignment techniques

• Proficiency in Python and PyTorch

• Rigorous mathematical foundations: linear algebra, probability theory, information theory, stochastic optimization

• Bilingual English/Mandarin to coordinate with overseas partners and stakeholders

Strongly Preferred

• First-author publications at top-tier AI/ML conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, AAAI) on LLM/Agent core topics — reasoning, multi-agent systems, RL for LLMs, post-training, agent memory, or related areas
• Research or engineering experience at a top-tier AI lab or team
• Experience designing reward functions, training pipelines, or evaluation frameworks for LLM-based agents — not just using existing APIs but building the core systems
• AI-native development workflow — using tools like Claude Code, Cursor, or Copilot Workspace as your primary development environment