As a research student , you will work on one of the teams core challenges in modern LLM-based systems, such as:
Agentic RAG
Advanced retrieval strategies, hybrid search, query rewriting, context optimization, and LLM-driven reasoning pipelines
Automatic Evaluation of Agents
LLM-as-a-Judge, scoring frameworks, hallucination detection, and robust evaluation methodologies
Agents for Knowledge Creation
Building agents that autonomously generate, refine, and maintain enterprise knowledge bases
Ontology-Grounded RAG
Enhancing models with domain-specific knowledge and structured representations
Data Analysis Agents
Agents capable of analyzing complex operational, product, and support data at scale
Fine-tuning LLMs for customer domain adaptation
Automatic agent improvement from user feedback including learning from errors, evaluations, and human signals
You will work one day per week, embedded directly in the Ask-AI research group, collaborating closely with senior researchers and engineers.
Requirements: Hands-on experience building real-world research systems at scale
Close mentorship from Ask-AIs research scientists
Opportunity to publish research based on project contributions
Experience working with state-of-the-art LLMs, retrieval engines, and agentic systems
This position is open to all candidates.