Binance Accelerator Program - LLM Model Training & Data Processing

Binance is the global blockchain company behind the world’s largest digital asset exchange by trading volume and users, serving a greater mission to accelerate cryptocurrency adoption and increase the freedom of money.
Are you looking to be a part of the most influential company in the blockchain industry and contribute to the crypto-currency revolution that is changing the world?
About Binance Accelerator Program
Binance Accelerator Program is a concise fixed-term program designed for Early Career Talent to have an immersive experience in the rapidly expanding Web3 space. You will be given the opportunity to experience life at Binance and understand what goes on behind the scenes of the worlds’ leading blockchain ecosystem. Alongside your job, there will also be a focus on networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about BAP Program HERE.
Who may apply
Current University students and recent graduates.

Responsibilities:

• Assist in the training, fine-tuning, and evaluation of Large Language Models (LLMs) using public and in-house datasets.
• Support the development and optimization of AI agents, including prompt engineering, memory modules, planning strategies, and integration with external tools.
• Design, implement, and manage data annotation pipelines, including schema definition, labeling guidelines, and quality control processes.
• Work closely with research and engineering teams to improve model performance, scalability, and robustness.
• Conduct experiments, perform data analysis, and clearly document methodologies and findings.
• Explore and test new tools, frameworks, and best practices for enhancing LLM systems and AI agent capabilities.

Requirements:

• Currently pursuing or recently completed a Degree in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. PHD is Bonus.
• Solid understanding of machine learning and deep learning fundamentals.
• Familiarity with transformer models, LLMs (e.g., LLaMA, Qwen), or related technologies is a strong plus.
• Experience or interest in prompt engineering, fine-tuning methods (e.g., LoRA, QLoRA), and model evaluation techniques.
• Basic knowledge of data annotation workflows and labeling tools.
• Strong analytical and problem-solving skills; able to work both independently and collaboratively.
• Fluency in English is required to be able to coordinate with overseas partners and stakeholders. Additional languages would be an advantage.