Agentic AI R&D Intern

What you'll do

• Conduct novel research in Agentic AI, Large Language Models, and related machine learning fields.
• Design, implement, and evaluate research prototypes demonstrating innovative AI agent capabilities.
• Address challenging industrial problems in manufacturing, mobility, and smart home domains.
• Analyze experimental results to identify specific opportunities for scientific innovation.
• Publish research outcomes in top-tier AI conferences and journals like NeurIPS and ICML.
• Develop agent systems for reasoning, planning, memory, and long-horizon task execution.
• Implement techniques for LLM post-training, reinforcement learning, and test-time scaling.
• Evaluate agent reliability, safety, and benchmarking across various real-world applications.

What we're looking for

• Currently enrolled in a Ph.D. or Master's program in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, or a related field.
• Minimum GPA of 3.0.
• Strong research background in Agentic AI, Large Language Models (LLMs), machine learning, natural language processing, or related areas.
• Experience developing AI agents using modern frameworks and ecosystems.
• Strong programming skills in Python and experience with modern AI/ML frameworks.
• Familiarity with agent workflows involving tool use, function calling, retrieval, memory, and multi-step reasoning.
• Experience conducting independent research, designing experiments, and analyzing results.
• Strong written and verbal communication skills and ability to work effectively in a collaborative research environment.
• Publication record in top-tier AI, NLP, or machine learning venues (preferred).
• Research experience in LLM-based agents, agent architecture, task planning, reasoning, and autonomous decision making (preferred).
• Research experience in agent optimization, including post-training, reinforcement learning, preference optimization, and self-improvement (preferred).
• Experience with retrieval-augmented generation (RAG), agentic retrieval systems, and knowledge-grounded agents (preferred).
• Experience with multi-agent systems, agent collaboration, and distributed agent workflows (preferred).
• Experience with multi-modal foundation models involving language, vision, and structured data (preferred).
• Experience in reinforcement learning, online learning, or adaptive decision-making systems (preferred).
• Experience with agent benchmarking, evaluation methodologies, reliability, safety, or alignment (preferred).
• Experience in applying Agentic AI technologies to real-world domains such as engineering design, or industrial automation (preferred).
• Open-source contributions or demonstrated leadership in AI research communities (preferred).