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.