Directly building agentic AI tooling—working with LLMs, embeddings, RAG and transformers to enable agents to search, reason, and act on web data.
About the Role
Staff Applied AI Researcher leading applied research and system design for an agent-native search platform that enables AI agents to search and reason over web data in real time. Own and ship production-scale retrieval, ranking, and grounding solutions optimized for latency, reliability, and agent-driven workflows.
Job Description
Role
Nebius is hiring a Staff Applied AI Researcher to lead applied research and system design for an agent-native search platform—search built for AI agents rather than humans. You will define new approaches, own a significant part of the search stack (e.g., ranking or iterative retrieval), and ship measurable impact to production.
Key Responsibilities
- Design agent-native search systems spanning low-latency single-hop search to multi-step iterative planning and refinement.
- Drive applied research and technical direction across retrieval and ranking systems.
- Design and evolve multi-stage retrieval architectures (query understanding, rewriting, retrieval, reranking, iterative retrieval).
- Lead experimentation on modern retrieval approaches (embeddings, sparse and hybrid retrieval, reranking, context compression) and productionize results.
- Train ranking and retrieval models optimized for agent-driven workflows under real-world constraints.
- Develop methods for grounding LLMs in real-time web data at scale using trust, quality, and freshness signals.
- Partner with evaluation to define new metrics for agentic systems where correctness is not reducible to clicks.
- Analyze trade-offs between relevance, latency, and cost; collaborate with engineering to deploy systems at web scale.
- Mentor researchers and engineers and raise the technical bar of the team.
Requirements
- 8+ years of experience in applied AI, ML, or software engineering.
- Proven track record of shipping ML/AI systems to production at scale.
- Deep experience with search, retrieval, ranking, or building RAG-based 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.
- Top-tier publications (ICLR, ICML, NeurIPS, AAAI, EMNLP, ACL).
- Ability to operate in a fast-moving, product-driven environment with high ownership and autonomy.
Nice to Haves
- Background in agentic AI systems (agents, tool use, autonomous workflows).
- Experience with RL post-training of LLMs or VLMs.
Benefits
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams