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# Staff Applied AI Researcher

- **Company:** Nebius
- **Location:** Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Computer Programming, Information Retrieval, Python (Programming Language), Open Source Technology, Recommender Systems, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Deep Learning, Low Latency, Machine Learning Operations
- **Published:** October 18, 2026
- **Apply:** https://startup.jobs/staff-applied-ai-researcher-agentic-search-nebius-8958417

## About the Role

* 8+ years of experience in applied AI, ML, or software engineering
 * Proven track record of shipping ML or AI systems to production at scale
 * Deep experience with search, retrieval, ranking, recommendation systems, or assistants
 * Strong understanding of modern deep learning, especially transformers and embeddings
 * Experience with LLM integrated or knowledge intensive systems
 * Experience designing evaluation frameworks and metrics for ML systems
 * Strong programming skills in Python and at least one of Go, C++, or similar
 * Ability to operate in a fast moving, product driven environment with high ownership and autonomy

Nice to haves

 * Experience with large scale search or recommendation systems
 * Background in agentic AI systems (agents, tool use, autonomous workflows)
 * Experience with RAG, multi step retrieval, or tool use
 * Publications, open source, or similar signals of technical depth and impact, Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

## Description

* 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, where correctness is not reducible to clicks
 * Lead experimentation on modern retrieval approaches (embeddings, hybrid search, reranking) and bring them into production
 * Analyse 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