Staff / Principal Applied AI Researcher (Agentic Search)

Nebius is 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. The role involves driving applied research and technical direction across retrieval and ranking systems, designing multi-stage retrieval architectures, and developing methods for grounding LLMs in real-time web data at scale. Requires 8+ years of experience in applied AI, ML, or software engineering, with deep experience in search, retrieval, ranking, or recommendation systems.

Core AIHybridFull-timePrincipalPythonGo

Staff / Principal Applied AI Researcher (Agentic Search)

Nebius is 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. The role involves driving applied research and technical direction across retrieval and ranking systems, designing multi-stage retrieval architectures, and developing methods for grounding LLMs in real-time web data at scale. Requires 8+ years of experience in applied AI, ML, or software engineering, with deep experience in search, retrieval, ranking, or recommendation systems.

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Core AIHybridFull-timePrincipalPython

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Hybrid

Experience Required

8 years

Employment Type

Full-time

Experience Level

Staff or Principal

Core Qualifications

Technical (Must-have)

PythonGoC++Machine LearningDeep LearningTransformersEmbeddingsSearchRetrievalRankingRecommendation SystemsLLMEvaluation FrameworksMetricsProduction

Soft Skills

OwnershipAutonomyMentoringCollaborationInnovationFast-movingBold thinkingConstant growthMeaningful impactTrust

Preferred Qualifications

Technical (Nice-to-have)

Large-scale SearchRecommendation SystemsAgentic AIRAGMulti-step RetrievalTool UseOpen Source

Key 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
•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

Applied AIAgentic SearchRetrievalRankingLLMMachine LearningStaffPrincipalHybridFull-time