Research Engineer, Agentic Retrieval (EMEA)
Qdrant · Remote (EMEA) · Developer Relations
About this role
Qdrant is hiring a mid-level Research Scientist in the machine learning function as a remote position. The posting calls out experience with RAG, Vector Database, LLMs, System Design.
- Role
- Research Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Remote (EMEA)
- Work mode
- Remote
- Department
- Developer Relations
- Posted
- May 8, 2026
More roles at Qdrant
Job description
from Qdrant careersQdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations.
Trusted by global leaders like Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom, we’re building the retrieval infrastructure layer for modern AI. Recently raising $50M in Series B funding, we are growing rapidly and committed to transforming how AI understands and interacts with data.
As a remote-first company, we believe diverse backgrounds, perspectives, and experiences fuel innovation. Here, you’ll own meaningful work, tackle challenges, and grow alongside passionate individuals dedicated to shaping the future of AI.
We are looking for a Research Engineer, Agentic Retrieval. You'll work at the seam between agent systems research and retrieval engineering, running a tight loop between hypothesis, experiment, and shipped artifact.
The questions you'll chase may not have settled answers yet: how agents should structure memory, when they should re-query versus reason, how skills and tools should be retrieved and composed, what retrieval primitives the agent loop actually needs, and what "good" even means when success is a multi-step trajectory rather than a ranked list.