Staff / Principal 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 R&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&D team.

The role

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.

You can think of this as Google 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.

We are designing how AI agents - not humans - retrieve, evaluate, and reason over web data in real time, under strict latency and reliability constraints. This means solving retrieval and ranking 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 by 7.

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.

What you’ll work on

* Designing agent native retrieval systems optimised for machine consumption rather than human search UX

* Building systems where LLMs iteratively plan, query, refine, and reason over results

* Developing ranking and retrieval approaches for multi step, agent driven workflows under real world constraints

Your responsibilities

* 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

Must haves

* 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