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

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

Competitive salary and comprehensive benefits package.
Opportunities for professional growth within Nebius.
Flexible working arrangements.
A dynamic and collaborative work environment that values initiative and innovation.

Applied AI Research Lead

Key skills