Product manager in search infrastructure

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.

• Set the quality bar and release criteria for search: what good means per use case, and what a change has to clear before it ships. Our evaluation team owns the measurement, and you work closely with them on what to measure and why.
• Own the depth, latency, and cost tradeoffs across our search products, and make them explicit and defensible.
• Shape the direction of our index: what it needs to cover, how fresh it needs to be, and what quality bar it has to meet.
• Build a point of view on where search infrastructure is going as agents, rather than people, become the main consumer, and turn that into a roadmap.
• Know how we compare to the alternatives customers evaluate, and drive the work that closes the gaps that matter.
• Work with customer success and directly with customers to understand where our results fall short in real workflows, and turn that into quality priorities.
• Partner with the data and vertical product owners on what our corpus needs to contain and how results should be shaped per industry.

• 5+ years of product management experience, with a meaningful portion spent on high-scale search or ranking systems: web search, feed ranking at a large social platform, or a large ad engine.
• A background as an engineer, ML engineer, or researcher on a production system in one of those areas. This is a requirement rather than a preference. You will be making tradeoff calls alongside senior search engineers, and you need to have built something at that scale yourself.
• Direct experience shipping a search or ranking change end to end, including how it was evaluated and what happened after it shipped.
• A strong data foundation: you can design an experiment, read eval results critically, and tell a real quality improvement from noise.
• Product judgment to match the technical depth: you can decide what to build and what to leave, and defend it to both engineers and customers.
• Strong understanding of LLMs, AI agents, and how retrieval fits into agentic workflows.
• Comfort with ambiguity, moving between strategy and execution without losing the thread.
• The ability to influence and align cross-functional stakeholders without formal authority.
• Будет плюсом: Depth in information retrieval, NLP, or applied machine learning beyond what the role requires; Experience with index tiering, sharding, freshness pipelines, or cost-per-query optimisation at scale; Experience with hybrid retrieval, semantic and lexical search, re-ranking, or human evaluation programs; Experience in API-first or developer-platform businesses running both self-serve and enterprise motions; Experience in a high-growth technology company, especially post-acquisition or startup-within-a-larger-org environments

• 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
• Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI