About the Role

As a Research Intern at NeoCognition, you'll explore novel ideas and work on longer-term research bets that push the boundaries of LLM agents. This internship is designed for those who want to dive deep into open research problems — from reasoning and planning to multi-agent coordination and evaluation.

You'll have the freedom to pursue high-risk, high-reward directions that may not be tied to immediate product needs, but could shape the future of agentic AI systems. Your work will culminate in research prototypes and, ideally, publications that contribute to the broader AI research community.

You'll collaborate closely with our research scientists and engineers, receiving mentorship and feedback as you design experiments, build prototypes, and analyze results.

Responsibilities

Explore novel research directions in areas such as LLM reasoning, planning, tool use, multi-agent systems, or evaluation methodologies.
Design and execute exploratory experiments to test new hypotheses and push the boundaries of what agentic systems can do.
Build research prototypes that demonstrate new capabilities or insights, even if they are not immediately production-ready.
Collaborate with the research team to document findings, analyze results, and iterate on ideas.
Work toward publishing research outcomes in top-tier AI venues (NeurIPS, ICLR, ICML, ACL, etc.) or contributing to open-source efforts.
Participate in team discussions, paper readings, and brainstorming sessions to shape the research roadmap.

Qualifications

Required:

Currently pursuing or recently completed a PhD, Master's, or advanced undergraduate degree in machine learning, computer science, or a related field.
Strong foundation in machine learning and natural language processing, with demonstrated interest in large language models or agentic AI systems.
Proficiency in Python and familiarity with modern ML frameworks (e.g., PyTorch, JAX, or TensorFlow).
Ability to design, implement, and analyze research experiments independently and collaboratively.
Strong written and verbal communication skills, with a passion for sharing ideas and learning from others.

Nice to have:

Prior research experience or publications in AI, NLP, or related areas.
Experience with open-weight models, fine-tuning, or reinforcement learning.
Familiarity with agent frameworks, tool-use systems, or evaluation benchmarks.
Interest in long-term research bets and comfort with ambiguity and exploration.

About the Role

You'll collaborate closely with our research scientists and engineers, receiving mentorship and feedback as you design experiments, build prototypes, and analyze results.

Responsibilities

Explore novel research directions in areas such as LLM reasoning, planning, tool use, multi-agent systems, or evaluation methodologies.

Design and execute exploratory experiments to test new hypotheses and push the boundaries of what agentic systems can do.

Build research prototypes that demonstrate new capabilities or insights, even if they are not immediately production-ready.

Collaborate with the research team to document findings, analyze results, and iterate on ideas.

Work toward publishing research outcomes in top-tier AI venues (NeurIPS, ICLR, ICML, ACL, etc.) or contributing to open-source efforts.

Participate in team discussions, paper readings, and brainstorming sessions to shape the research roadmap.

Required:

Currently pursuing or recently completed a PhD, Master's, or advanced undergraduate degree in machine learning, computer science, or a related field.

Strong foundation in machine learning and natural language processing, with demonstrated interest in large language models or agentic AI systems.

Proficiency in Python and familiarity with modern ML frameworks (e.g., PyTorch, JAX, or TensorFlow).

Ability to design, implement, and analyze research experiments independently and collaboratively.

Strong written and verbal communication skills, with a passion for sharing ideas and learning from others.

Nice to have:

Prior research experience or publications in AI, NLP, or related areas.

Experience with open-weight models, fine-tuning, or reinforcement learning.

Familiarity with agent frameworks, tool-use systems, or evaluation benchmarks.

Interest in long-term research bets and comfort with ambiguity and exploration.