Binance Accelerator Program - LLM Model Training & Data Processing

    Asia && Australia, Sydney && Australia, Melbourne && UAE, Dubai && Hong Kong && Taiwan, Taipei
    Full-Time
    Mid (3-6 yrs)
    Product Management
    Posted on January 22, 2026
    Responsibilities
  1. Assist in the training, fine-tuning, and evaluation of Large Language Models (LLMs) using public and in-house datasets.
  2. Support the development and optimization of AI agents, including prompt engineering, memory modules, planning strategies, and integration with external tools.
  3. Design, implement, and manage data annotation pipelines, including schema definition, labeling guidelines, and quality control processes.
  4. Work closely with research and engineering teams to improve model performance, scalability, and robustness.
  5. Conduct experiments, perform data analysis, and clearly document methodologies and findings.
  6. Explore and test new tools, frameworks, and best practices for enhancing LLM systems and AI agent capabilities.
  7. Requirements
  8. Currently pursuing or recently completed a Degree in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. PHD is Bonus.
  9. Solid understanding of machine learning and deep learning fundamentals.
  10. Familiarity with transformer models, LLMs (e.g., LLaMA, Qwen), or related technologies is a strong plus.
  11. Experience or interest in prompt engineering, fine-tuning methods (e.g., LoRA, QLoRA), and model evaluation techniques.
  12. Basic knowledge of data annotation workflows and labeling tools.
  13. Strong analytical and problem-solving skills; able to work both independently and collaboratively.
  14. Fluency in English is required to be able to coordinate with overseas partners and stakeholders. Additional languages would be an advantage.
  15. Company:  Binance

    Operates the world's largest cryptocurrency exchange, offering trading, staking, and blockchain ecosystem services to 270M+ users globally.
    5001-10000 employees
    Finance & Fintech
    HQ: None (Global)