Binance Accelerator Program - LLM Model Training & Data Processing
- Taipei City
- Permanent
- Full-time
- 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.
- Currently pursuing or recently completed a degree in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline.
- 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.
- Shape the future with the world's leading blockchain ecosystem
- Collaborate with world-class talent in a user-centric global organization with a flat structure
- Tackle unique, fast-paced projects with autonomy in an innovative environment
- Thrive in a results-driven workplace with opportunities for career growth and continuous learning
- Competitive salary and company benefits
- Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)