Research Engineer, Agentic Retrieval
Q
QdrantAI
Remote - EMEA
Secondary Locations: Spain, France, Germany, NetherlandsFull-Time
Salary not disclosed
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Job Details
Requirements
- You read and reason about LLM behavior directly.
- You treat memory as a systems design problem.
- You understand tool and skill systems as retrieval problems.
- You have a working theory of context engineering.
- You build evals before features.
- You know vector search internals at a decent level.
- You write precisely.
Responsibilities
- Define what good agentic retrieval looks like.
- Characterize the retrieval patterns inside real agent loops, name the failure modes, and turn that vocabulary into something the team and the field can build against.
- Treat agent memory as a systems problem.
- Investigate skill and tool retrieval as a first-class problem.
- Design and run experiments on retrieval inside agent loops: query rewriting and decomposition, multi-hop retrieval, tool-conditioned filtering, retrieval-as-a-tool patterns, and the interplay between planner, retriever, and reranker.
- Build evaluation infrastructure for agentic retrieval.
- Profile agent retrieval traces end to end.
- Study how real agent stacks use Qdrant in production.
- Pair with design-partner teams running serious agent workloads in production, and bring their real constraints back into research priorities.
- Influence the roadmap.
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