Pioneer Talent Program - Research Data Scientist

About the role

This Research Data Scientist position involves conducting original research into generative AI and large language models, specifically as they apply to crypto and financial domains. The work includes exploring areas such as reasoning under market uncertainty, aligning trading agents, and multi-agent coordination. A key part of the role is to design and execute rigorous experiments, formulating hypotheses, implementing various model and training approaches, and drawing statistically sound conclusions from the results.

The role also requires developing novel post-training methodologies and evaluation frameworks tailored for LLMs operating within crypto contexts. This covers assessing chain-of-thought quality for market analysis, ensuring agent decision consistency, and building robustness against adversarial prompt injections. Additionally, the scientist will research and apply test-time scaling techniques, such as process reward models and Monte Carlo Tree Search-based planning, to enhance agent reasoning in ambiguous and fast-moving market conditions.

Success in this position means staying critically informed about developments in the broader research community, including major conferences like NeurIPS and ICML, as well as crypto-adjacent venues. The goal is to identify high-leverage opportunities to integrate state-of-the-art techniques into Binance's specific challenges. The work environment is described as user-centric and global, offering autonomy on unique, fast-paced projects within an innovative setting.

The annual salary for this position ranges from $150,000 to $200,000 USD.

Skills that matter here

• Machine Learning: This role requires a deep understanding of ML principles to conduct original research and develop novel methodologies for AI applications in crypto.
• AI: The core of this position involves applying advanced AI techniques, particularly generative AI and large language models, to solve complex financial problems.
• Python: Proficiency in Python is essential for implementing models, conducting experiments, and developing AI-native solutions using modern coding practices.
• PyTorch: Experience with PyTorch is necessary for developing and training large language models and other AI architectures relevant to the research.
• transformer architectures: A deep understanding of transformer architectures is critical for working with large language models and their pretraining dynamics.
• RLVR-based reasoning: The role involves implementing and researching RLVR-based reasoning approaches for model training and improving agent decision-making.

Who this role suits

• A candidate with a strong academic background, ideally a Master's or PhD in a quantitative field, who has 0-5 years of research or industry experience in ML/AI.
• Someone who possesses rigorous mathematical foundations in areas like linear algebra, probability theory, and stochastic processes.
• An individual who is proficient in Python and PyTorch, and adopts an AI-native development workflow using tools like Claude Code or Copilot Workspace.
• A person who is bilingual in English and Mandarin, necessary for coordinating with international partners and stakeholders.

From the employer

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.

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).