About the company
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.
In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible. In this role, you will define what quality means for a search system built for AI agents from day one.
Responsibilities
- Own search-related quality metrics end-to-end: from definition and computation to evolution and transition to next-generation metrics.
- Design and reason about analytics architecture across the full stack: runtime behaviour → data pipelines → metrics → dashboards.
- Develop and maintain analytics pipelines and tooling using Python.
- Translate product, user, and technical requirements into meaningful, actionable metrics.
- Challenge existing assumptions about quality and push for better, more reliable measurement.
- Work closely with product managers, engineers, and stakeholders to align analytics with system behavior and business goals.
- Debug metric anomalies, investigate quality regressions, and propose data-driven improvements.
Requirements
- Senior-level experience solving search or search-adjacent analytical problems.
- Strong exposure to quality metrics in at least one of the following areas: search relevance (web, geo, product, offers), ranking and freshness, ads quality or feed ranking.
- Ability to think end-to-end across users, products, and technology, not just individual metrics.
- Solid foundation in mathematical statistics.
- Strong Python skills and a hands-on engineering mindset.
- Experience operating in ambiguous environments and driving solutions independently.
- Clear analytical judgment: understanding why a metric exists, how it evolves, and when it should be retired.
- Strong communication skills and the ability to align product, engineering, and client perspectives.
Conditions
- 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