Nebius logo

Staff / Principal Applied AI Researcher – Agentic Search

Verified
Nebius
full-time

About the Role

<div class="content-intro"><p><strong data-stringify-type="bold">Why work at Nebius<br></strong>Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.</p> <p><strong>Where we work<br></strong>Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&amp;D hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&amp;D team.</p></div><p></p> <p data-start="77" data-end="227">We are 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.</p> <p data-start="229" data-end="481">This is not traditional search. We are designing how AI agents - not humans - access, retrieve, and reason over information on the internet. As AI becomes the primary interface to the web, this layer will replace the role of traditional search engines.</p> <p data-start="483" data-end="577">This means solving retrieval and search problems under entirely new access patterns and scale.</p> <p data-start="579" data-end="743">Depending on your experience and scope, this role can be positioned at Staff or Principal level, with ownership over key parts of our applied AI research direction.</p> <p data-start="745" data-end="934">You will lead applied research that directly improves how AI systems retrieve, ground, and use real-world information in production, with research tightly coupled to large-scale deployment.</p> <p data-start="936" data-end="959"><strong data-start="936" data-end="959">What you’ll work on</strong></p> <p data-start="961" data-end="1103">You will work on problems at the intersection of search, retrieval, and LLM-based systems, helping define how AI agents interact with the web.</p> <p data-start="1105" data-end="1424">This includes:<br>• Designing agent-native retrieval systems (not human search UX)<br data-start="1184" data-end="1187">• Building systems where LLMs actively query, iterate, and reason over results<br data-start="1265" data-end="1268">• Developing ranking and retrieval approaches optimized for agent workflows, not clicks<br data-start="1355" data-end="1358">• Defining new evaluation paradigms for AI systems using the web</p> <p data-start="1426" data-end="1451"><strong data-start="1426" data-end="1451">Your responsibilities</strong></p> <p data-start="1453" data-end="2205">• Drive applied research across retrieval, ranking, and agent-centric search systems<br data-start="1537" data-end="1540">• Design and evolve multi-stage retrieval systems (query understanding, rewriting, reranking, iterative retrieval)<br data-start="1654" data-end="1657">• Develop methods for grounding LLMs in real-time web data at scale<br data-start="1724" data-end="1727">• Define evaluation frameworks and quality metrics for agent-native workloads<br data-start="1804" data-end="1807">• Lead experimentation on modern retrieval approaches (hybrid search, embeddings, cross-encoders)<br data-start="1904" data-end="1907">• Work closely with engineering to bring research into production in high-throughput systems<br data-start="1999" data-end="2002">• Analyse trade-offs across relevance, latency, and cost at scale<br data-start="2067" data-end="2070">• Contribute to long-term research and product direction<br data-start="2126" data-end="2129">• Mentor engineers and researchers and raise the technical bar of the team</p> <p data-start="2207" data-end="2221"><strong data-start="2207" data-end="2221">Must-haves</strong></p> <p data-start="2223" data-end="3042">• 8+ years of experience in applied AI, ML, or software engineering<br data-start="2290" data-end="2293">• Proven track record of shipping ML/AI systems into production at scale<br data-start="2365" data-end="2368">• Deep experience in search, retrieval, ranking, recommendation systems, or assistants<br data-start="2454" data-end="2457">• Strong understanding of modern deep learning (transformers, embeddings, ranking models)<br data-start="2546" data-end="2549">• Experience working on LLM-integrated or knowledge-intensive systems<br data-start="2618" data-end="2621">• Experience designing evaluation frameworks and metrics for ML systems<br data-start="2692" data-end="2695">• Strong programming skills (Python + at least one of Go / C++ / similar)<br data-start="2768" data-end="2771">• Ability to operate in a product-driven, fast-moving environment<br data-start="2836" data-end="2839">• Strong ownership and ability to drive ambiguous problems end-to-end<br data-start="2908" data-end="2911">• You have built or significantly contributed to large-scale production systems (search, recommendations, assistants, or similar)</p> <p data-start="3044" data-end="3061"><strong data-start="3044" data-end="3061">Nice-to-haves</strong></p> <p data-start="3063" data-end="3511" data-is-last-node="" data-is-only-node="">• Experience with large-scale search systems (web search, ads, marketplaces, assistants)<br data-start="3151" data-end="3154">• Background in agentic AI systems (coding agents, research agents, tool use)<br data-start="3231" data-end="3234">• Experience with RAG systems, multi-step retrieval, and tool use<br data-start="3299" data-end="3302">• Familiarity with query understanding, personalization, or recommendation systems<br data-start="3384" data-end="3387">• Publications, conference talks, or open-source contributions</p> <p></p><div class="content-conclusion"><p><strong>What we offer</strong>&nbsp;</p> <ul> <li>Competitive salary and comprehensive benefits package.</li> <li>Opportunities for professional growth within Nebius.</li> <li>Flexible working arrangements.</li> <li>A dynamic and collaborative work environment that values initiative and innovation.</li> </ul> <p><span data-contrast="auto">We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!</span></p></div>

Ready to apply?

Click below to apply directly on Nebius's careers page.

Get the top 10 hyper-growth roles delivered to your inbox every Tuesday.