Senior Product Manager, Search Infrastructure
About this role
The Tavily team at Nebius builds the web access layer for AI agents, powering infrastructure used by over two million developers and Fortune 500 companies. This group operates its own index to handle crawl coverage at scale, freshness, cost per query, and retrieval quality.
As the Senior Product Manager for Search Infrastructure, you will own this infrastructure as a product, balancing the competing demands of relevance, freshness, speed, and cost. You will define the quality bar for different use cases, partner with evaluation teams to establish release criteria, and shape the future roadmap as search evolves for autonomous agents rather than human consumers. This is a hands-on individual contributor role reporting to the Director of Product, working closely with search engineers, researchers, customer success, and vertical product owners.
Ideal candidates bring a technical background as an engineer or researcher combined with product management experience in high-scale search, ranking, or ad systems. You should thrive in a collaborative environment where you can argue technical tradeoffs directly with experienced engineers and translate customer feedback into concrete product priorities.
What you will do
- Establish the quality bar and release criteria for search products by defining what good means for each use case.
- Manage the depth, latency, and cost tradeoffs across search infrastructure to make them explicit and defensible.
- Shape the strategic direction of the index regarding coverage, freshness, and quality standards.
- Formulate a roadmap based on a clear point of view of how search infrastructure must adapt for AI agents.
- Analyze competitive alternatives and drive initiatives to close relevant product gaps.
- Collaborate with customer success and users directly to identify quality shortfalls in real workflows and set priorities.
- Partner with data and vertical product owners to define corpus requirements and industry-specific result shaping.
What you need
- Have at least 5 years of product management experience, focusing heavily on high-scale search, ranking systems, web search, social platform feed ranking, or large ad engines.
- Possess a background as an engineer, machine learning engineer, or researcher on a production system within those domains.
- Demonstrate direct experience shipping a search or ranking change end to end, including evaluation and post-shipment analysis.
- Maintain a strong data foundation to design experiments, critically read evaluation results, and differentiate quality improvements from noise.
- Bring product judgment capable of deciding what to build and what to omit, while defending those choices to engineers and customers.
- Show a strong understanding of large language models, AI agents, and how retrieval integrates into agentic workflows.
Nice to have
- Depth in information retrieval, NLP, or applied machine learning beyond core requirements
- Experience with index tiering, sharding, freshness pipelines, or cost-per-query optimization 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 self-serve and enterprise motions
- Experience in high-growth technology companies, startups, or post-acquisition environments
Benefits
- 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
Skills and tech stack
Questions candidates ask
Who does this role report to?
This position reports directly to the Director of Product.
What team will I be working with on a day-to-day basis?
You will work day to day with search engineering and research teams, the evaluation team, and customer success.
What is the primary focus of the infrastructure product?
The infrastructure serves as the web access layer for AI agents, focusing on crawl coverage at scale, freshness, cost per query, and result quality.