Staff / Principal Applied AI Researcher (Agentic Search)

# Staff or Principal Applied AI Researcher

Nebius is seeking a Staff or Principal Applied AI Researcher to join a fast-growing team building an agent-native search platform - the web access layer for AI systems.

You'll work on designing how AI agents retrieve, evaluate, and reason over web data in real time, under strict latency and reliability constraints. This involves solving retrieval and ranking under entirely new access patterns and at significant scale, with systems operating over constantly changing, unstructured data serving tens of thousands of production workloads 24/7.

## What you'll work on:

• Designing agent-native retrieval systems optimized for machine consumption rather than human search UX
• Building systems where LLMs iteratively plan, query, refine, and reason over results
• Developing ranking and retrieval approaches for multi-step, agent-driven workflows under real-world constraints

## Your responsibilities:

• Drive applied research and technical direction across retrieval and ranking systems
• Design and evolve multi-stage retrieval architectures (query understanding, rewriting, reranking, iterative retrieval)
• Develop methods for grounding LLMs in real-time web data at scale
• Define and implement new evaluation paradigms and metrics for agentic systems
• Lead experimentation on modern retrieval approaches (embeddings, hybrid search, reranking) and bring them into production
• Analyze trade-offs across relevance, latency, and cost at scale
• Work closely with engineering to deploy systems in high-throughput, low-latency environments
• Own ambiguous problems end-to-end and contribute to product and research direction
• Mentor engineers and help raise the technical bar of the team