Research Data Scientist
New
B
BinanceAI, cryptocurrency
Location: Hong Kong; Singapore; TaiwanFull-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- Bilingual English/Mandarin
- Experience
- 0–5 years of research or industry experience in ML/AI
- Required Skills
- PythonPyTorchLLMGenerative AI
Requirements
- Master's or PhD in Machine Learning, Computer Science, Mathematics, Statistics, or a related field; exceptional Bachelor's candidates with demonstrable research output will be considered.
- 0–5 years of research or industry experience in ML/AI; academic lab or research internship experience is valued.
- Deep understanding of transformer architectures, LLM pretraining dynamics, and post-training methodology, including RLHF, RLVR-based reasoning model training, and alignment techniques.
- Proficiency in Python and PyTorch.
- Strong foundations in linear algebra, probability theory, information theory, and stochastic optimization.
- Bilingual English/Mandarin proficiency to coordinate with overseas partners and stakeholders.
- First-author publications at top-tier AI/ML conferences on LLM or agent topics such as reasoning, multi-agent systems, RL for LLMs, post-training, or agent memory.
- 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, including building core systems rather than only using existing APIs.
- Use an AI-native development workflow with tools such as Claude Code, Cursor, or Copilot Workspace as the primary development environment.
Responsibilities
- Conduct original research in generative AI and large language models applied to crypto and financial domains, including market uncertainty, trading-agent alignment, multi-agent coordination, and test-time scaling.
- Design and execute experiments by formulating hypotheses, implementing model and training variants, running ablations, and drawing statistically sound conclusions.
- Develop post-training methodologies and evaluation frameworks for LLMs operating in crypto contexts.
- Evaluate chain-of-thought quality for market analysis, agent decision consistency, and robustness against adversarial prompt injection.
- Research process reward models, self-consistency, and Monte Carlo Tree Search-based planning to improve agent reasoning.
- Track research developments at NeurIPS, ICML, ICLR, ACL, EMNLP, and crypto-adjacent venues.
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