Binance Accelerator Program - LLM Model Training & Data Processing

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. Binance is trusted by more than 320 million people in 100+ countries for its industry-leading security, transparency, trading engine speed, protections for investors, and unmatched portfolio of digital asset products and offerings from trading and finance to education, research, social good, payments, institutional services, and Web3 features. Binance is devoted to building an inclusive crypto ecosystem to increase the freedom of money and financial access for people around the world with crypto as the fundamental means.

About Binance Accelerator Program

Binance Accelerator Program (BAP) is a 3-6 month internship program designed for Early Career talent to have firsthand experience in the rapidly expanding digital assets space. You will be given the opportunity to develop your skills at Binance and understand what it’s like to work at the world's leading blockchain ecosystem. As part of your internship in the BAP, there will also be opportunities for networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about the BAP Program HERE .

Who may apply

Current university students and recent graduates.

*Terms of employment / engagement shall be subject to contract and local applicable laws

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.