Research Student

Ask-AI is expanding its Research Team and is looking for MSc or PhD students to join us as part-time research students.

This is a unique opportunity to work closely with Ask-AI’s research scientists on cutting-edge problems at the intersection of NLP and agentic AI systems.

About Ask-AI

Ask-AI brings B2B GTM teams into the AI era by layering an agent-first architecture on a real Customer Context Model- enriching every interaction with accurate reasoning, signal extraction, and proactive insight.

Built on more than 100 deep integrations, our platform ships five core products- Knowledge, Assist, Intelligence, Alerting, and Self-Serve- all running on shared infrastructure. Each new product we launch would have been a standalone company in the pre-AI era; for us, it’s a focused product built on a common foundation, enabling rapid, reliable releases and compounding leverage.

Our no-code Agent Builder allows teams to design and deploy agents (models, tools, triggers, actions) with governance and traceability, while the context layer powers sentiment analysis, trend detection, risk identification, and real-time alerts- modernizing customer experience end-to-end without disrupting existing workflows.

About The Role

As a research student at Ask-AI, you will work on one of the team’s 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
  
  

• Hands-on experience building real-world research systems at scale
• Close mentorship from Ask-AI’s research scientists
• Opportunity to publish research based on project contributions
• Experience working with state-of-the-art LLMs, retrieval engines, and agentic systems
  
  

• Strong understanding of NLP, LLMs, and/or Information Retrieval
• At least one accepted paper in a reputable AI / NLP / ML conference or workshop
• (e.g., ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, or similar)
• Excellent communication and technical writing skills (including for potential publication)
• Availability to commit one full day per week during the semester