Senior Applied AI Researcher (Agentic Search)

Why work at Nebius 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. Where we work Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with RD hubs across Europe, North America, and Israel. The team of over 800 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI RD team. The product In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible. We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale. The role We are looking for a Senior Applied AI Researcher to work at the intersection of AI research, search quality, and product strategy. In this role, you will explore how modern AI systems and agents should interact with search, and translate those insights into practical improvements in retrieval, ranking, and answering quality. You will work on semantic retrieval, reranking, and evaluation, while also helping shape the long-term direction of the product. This is not a purely academic role. You will be expected to prototype, validate ideas on real systems, and collaborate closely with engineers and product leaders. Over time, you may help guide or mentor more research-focused contributors as the team grows. In this position, your responsibility will be to Explore and articulate how AI agents should use search, including new interaction patterns, query formulations, and evaluation criteria beyond traditional keyword relevance Conduct deep applied research on search quality, focusing on moving from “textual match” toward answering the underlying question or intent Train ranking and reranking models that optimize for answering intent and efficient resolution of agent queries, rather than purely textual similarity. Design, prototype, and evaluate ranking and reranking approaches, including neural and LLM-based methods Work on semantic retrieval and embedding-based ANN systems, including model selection, adaptation, and tradeoffs between recall, precision, and latency Define and evolve quality metrics and evaluation methodologies appropriate for agentic search and question answering Collaborate closely with backend and ML engineers to turn research ideas into production-ready components Partner with product managers to connect research insights with product strategy and roadmap decisions Serve as a technical thought leader for applied AI topics in the team, and help set direction for future research-oriented hires You may be a good fit if you: Have experience working on applied ML, NLP, or IR problems in production-facing systems Are comfortable moving between conceptual research and hands-on prototyping Have worked on or around search, ranking, retrieval, recommendation, or question answering systems Understand modern approaches to embeddings, reranking, and LLM-based reasoning, even if you are not training large models from scratch Enjoy thinking about product-level questions (“what should the system do?”), not just model internals Can communicate complex ideas clearly to engineers, product managers, and non-research stakeholders Have the maturity and curiosity to help shape a research direction, not just execute on predefined tasks Strong candidates may also have experience with: LLM-based ret ... (truncated, view full listing at source)