greenhouse
Senior Applied ML Engineer (Agentic Search)
Nebius
Remote, GBR
Full-time
Senior · 5+ years experience
Remote
Discovered Today
PythonGoC++
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About Nebius
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.
Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search.
Your responsibilities
- Design, train, and deploy ML models for retrieval, reranking, and search relevance in production
- Build and optimise embedding-based indexing and large-scale retrieval systems
- Develop models supporting crawling, data selection, and content understanding
- Define and improve quality metrics for agent-native search and build evaluation pipelines
- Work on systems operating at very large scale, including high-throughput query workloads
- Collaborate closely with engineering teams to integrate ML models into production services
- Analyse performance trade-offs across latency, quality, and cost
- Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems
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