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.