Applied AI Research Lead

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 1400 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. *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" data-turn-id="request-WEB:0d97fb2b-fbe8-44a5-8e11-c45a7e267879-6" data-testid="conversation-turn-12" data-scroll-anchor="true" data-turn="assistant"> We are seeking an Applied AI Research Lead to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. Depending on your experience and scope, this role can be scoped at Staff or Principal level, with the opportunity to act as a technical lead for applied AI research within the team. You will lead applied research that directly improves how AI systems retrieve, reason over, and use real-world information. This is a highly impactful role focused on production systems, where research is tightly coupled with real-world deployment at scale. You will work on problems at the intersection of search, retrieval, and LLM-based systems, shaping how AI agents access and interact with the web. This includes advancing retrieval pipelines, ranking systems, grounding techniques, and evaluation frameworks for agent-native workloads. Your responsibilities • Drive applied research across retrieval, ranking, and agent-centric search systems • Design and improve multi-stage retrieval pipelines, including query understanding, rewriting, and reranking • Develop approaches for grounding LLMs using real-time web data • Define and implement evaluation methodologies and quality metrics for agent-native search • Lead experimentation on modern retrieval techniques such as hybrid search, embedding-based systems, and cross-encoders • Work closely with engineering teams to bring research into production at scale • Analyse trade-offs across relevance, latency, and cost in large-scale systems • Contribute to long-term research and product direction • Mentor engineers and researchers and raise the technical bar of the team Must-haves • 8+ years of experience in applied AI, machine learning, or software engineering • Strong track record of shipping ML or AI systems into production, not purely research • Deep experience in retrieval, ranking, search relevance, or recommendation systems • Strong understanding of modern deep learning approaches including transformers and embeddings • Experience working with LLM-integrated systems or knowledge-intensive AI applications • Hands-on experience designing evaluation frameworks and defining meaningful metrics • Strong programming skills in Python, Go, or C++ • Ability to operate in a product-driven, fast-moving environment • Strong ownership and ability to drive ambiguous problems end-to-end Nice-to-haves • Experience with large-scale search systems such as web search, marketplaces, ads, or assistants • Background in agentic AI systems or AI agents such as coding or research agents • Familiarity with RAG systems, multi-step retrieval, and tool use • Experience with query understanding, personalization, or recommendation systems • Publications, conference talks, or open-source contributions • Participation in competitive programming or ML competitions such as Kaggle We conduct coding interviews as part of the process. What we offer Competitive salar ... (truncated, view full listing at source)