Staff Applied AI Researcher (Agentic Search)

<div class="content-intro"><p><strong>About Nebius:</strong></p> <p>Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.</p> <p>Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.</p> <p>Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.</p></div><p></p> <p data-renderer-start-pos="758" data-local-id="349f5d1d97ae">We are seeking a Staff Applied AI Researcher to join a fast-growing team building an agent-native search platform - the web access layer for AI systems.</p> <p data-renderer-start-pos="912" data-local-id="52da8de25c65">You can think of this as Google search, but for AI agents: a system designed for machines, not humans. We are building agentic search, where AI systems actively plan, retrieve, evaluate, and refine information rather than simply returning results. As AI becomes the primary interface to the web, this layer will replace the role of traditional search engines.</p> <p data-renderer-start-pos="1273" data-local-id="8ba2f733eb98">We are designing these systems to search and reason over web data in real time, under strict latency and reliability constraints. This means solving retrieval, ranking and passage extraction under entirely new access patterns and at significant scale, with systems operating over constantly changing, unstructured data and serving tens of thousands of production workloads 24/7.</p> <p data-renderer-start-pos="1653" data-local-id="dc8aefa0b2c5">This role comes with ownership over key parts of our applied AI research direction and system design, with a strong expectation of defining new approaches and shipping measurable impact in production. In the first year, you will own a specific part of the search stack, e.g. ranking or iterative retrieval.</p> <p data-renderer-start-pos="1961" data-local-id="3a68defafc73"><strong data-renderer-mark="true">What you'll work on:</strong></p> <ul class="ak-ul" data-local-id="b2d49ed7-2072-4623-afb5-0f3e46fb9eff" data-indent-level="1"> <li> <p data-renderer-start-pos="1985" data-local-id="dbde4a36f318">Designing an agent-native search system that handles everything from low-latency single-hop search to complex deep search tasks requiring iterative planning, querying, refinement and reasoning.</p> </li> <li> <p data-renderer-start-pos="2182" data-local-id="330562c2b43d">Training ranking and retrieval models optimized for agent-driven workflows under real world constraints</p> </li> <li> <p data-renderer-start-pos="2289" data-local-id="a80b20c7f5e2">Bringing the latest advances from the research community to our search stack and ship it to production.</p> </li> </ul> <p data-renderer-start-pos="2396" data-local-id="c5f3d6d01f8e"><strong data-renderer-mark="true">Your responsibilities:</strong></p> <ul class="ak-ul" data-local-id="ca556365-56d6-4b18-a6ab-e96d32aada2a" data-indent-level="1"> <li> <p data-renderer-start-pos="2422" data-local-id="ba12fa15e8c5">Drive applied research and technical direction across retrieval and ranking systems</p> </li> <li> <p data-renderer-start-pos="2509" data-local-id="0ac01c95d3f6">Design and evolve multi-stage retrieval architectures (query understanding, rewriting, retrieval, reranking and iterative retrieval)</p> </li> <li> <p data-renderer-start-pos="2645" data-local-id="bdd93b58a974">Lead experimentation on modern retrieval approaches (embedding research, sparse and hybrid retrieval, reranking, context compression) and bring them into production</p> </li> <li> <p data-renderer-start-pos="2813" data-local-id="5d99bb2639e8">Develop methods for grounding LLMs in real-time web data at scale, using trust, quality and freshness signals to surface reliable sources</p> </li> <li> <p data-renderer-start-pos="2954" data-local-id="5b539cf905a3">Partner with the evaluation team to define new metrics for agentic systems, where correctness is not reducible to clicks</p> </li> <li> <p data-renderer-start-pos="3078" data-local-id="7f6ed1a6999b">Analyze trade-offs across relevance, latency, and cost, and work closely with engineering to deploy systems that operate at web scale</p> </li> <li> <p data-renderer-start-pos="3215" data-local-id="98cbe59fe7ce">Mentor researchers and engineers and help raise the technical bar of the team</p> </li> </ul> <p data-renderer-start-pos="3296" data-local-id="4e7298218846"><strong data-renderer-mark="true">Must haves:</strong></p> <ul class="ak-ul" data-local-id="288927ca-3161-4538-89a9-be6a7978542a" data-indent-level="1"> <li> <p data-renderer-start-pos="3311" data-local-id="3c107fbda2c8">8+ years of experience in applied AI, ML, or software engineering</p> </li> <li> <p data-renderer-start-pos="3380" data-local-id="529c36f68cf3">Proven track record of shipping ML or AI systems to production at scale</p> </li> <li> <p data-renderer-start-pos="3455" data-local-id="a88ee0b3e468">Deep experience with search, retrieval, ranking or building RAG-based assistan