Research Scientist - LLM Foundation Models

Job description

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 offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world. **About the Role** We are seeking a highly skilled Research Scientist/Engineer to advance the reasoning and planning capabilities of large foundation models. 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 during pretraining, SFT, and RL stages. A key part of the role involves solving complex tasks using **System 2 thinking** and applying advanced decoding strategies such as MCTS and A\*. You will design and implement robust evaluation methodologies, teach models to interact with external tools, APIs, and code interpreters, and build agents and multi-agent systems capable of addressing sophisticated real-world problems. ### Responsibilities - Reasoning and planning for foundation models: Enhance reasoning and planning throughout the entire development process, including data acquisition, model evaluation, SFT, reward modeling, and reinforcement learning, to improve overall performance. - Synthesize large-scale, high-quality data using methods such as rewriting, augmentation, and generation to improve the capabilities of foundation models in various stages (pretraining, SFT, RL). - Solve complex tasks using system 2 thinking and leverage advanced decoding strategies such as MCTS, A\*. - Investigate and implement robust evaluation methodologies to assess model performance at various stages. - Teach foundation models to use tools, interact with APIs, and code interpreters. Build agents and multi-agent systems to solve complex tasks. ### Requirements - Proficiency in research experience with RL, LLM, and familiarity with large-scale model training is preferred. - Proficiency in data structures and fundamental algorithm skills, and fluency in Python or C++/Java. - Experience with influential projects or papers in RL, NLP, or Deep Learning is preferred. - Excellent problem analysis and problem-solving skills, capable of deeply addressing challenges in large-scale model training and application. - Good communication and collaboration skills, with the ability to explore new technologies with the team and promote technological progress.