AI Agent Research & Application Intern
Description
This role focuses on advancing AI Agent architecture, reasoning, and autonomy through applied research and experimentation. Interns will work on cutting-edge projects involving foundation models, planning algorithms, and multi-agent systems, contributing to both theoretical innovation and practical applications.
Responsibilities
- Conduct research on AI foundation models to enhance reasoning and data-driven analytical capabilities.
- Design and develop advanced planning and decision-making algorithms that enable Agents to autonomously perform complex tasks and improve operational efficiency.
- Investigate tool-use mechanisms to allow Agents to effectively integrate and utilize diverse data analysis and system tools for greater adaptability in real-world environments.
- Explore and implement end-to-end reinforcement learning frameworks and multi-agent collaboration algorithms to improve coordination and optimization across intelligent systems.
Requirements
- Master’s or PhD student in Computer Science, Mathematics, Statistics, Artificial Intelligence, or a related field.
- Strong foundation in mathematics (linear algebra, probability, statistics) and excellent problem-solving skills.
- Solid understanding of machine learning and deep learning algorithms (e.g., neural networks, decision trees, SVMs, reinforcement learning), with hands-on implementation experience.
- Proficiency in Python and familiarity with mainstream ML/DL frameworks such as TensorFlow and PyTorch, with the ability to independently develop and train models.
- Deep curiosity and passion for autonomous systems, reasoning, and multi-agent collaboration.
- Ability to bridge theoretical research and practical application.
- Experience with reinforcement learning, tool-augmented LLMs, or agentic workflows is a strong plus.
- Minimum 6-month full-time internship preferred.
