About the job
Join Nebius and Shape the Future of AI
Nebius is pioneering a transformative approach to cloud computing, dedicated to empowering the global AI economy. Our mission is to provide the essential tools and resources that enable our clients to tackle real-world challenges and revolutionize industries, all while minimizing infrastructure costs and the necessity of extensive in-house AI/ML teams. Our talented workforce operates at the forefront of AI cloud infrastructure, collaborating with some of the most innovative and experienced leaders and engineers in the industry.
Our Work Environment
Located in Amsterdam and publicly listed on Nasdaq, Nebius boasts a global presence with R&D centers across Europe, North America, and Israel. Our diverse team, comprising over 1,400 employees, includes more than 400 highly skilled engineers, equipped with deep expertise in both hardware and software engineering, as well as an in-house AI R&D team.
We are on the lookout for a Staff or Principal Applied AI Researcher to join our rapidly expanding team, focused on developing an agent-native search platform, the vital web access layer for AI systems.
Unlike traditional search methodologies, we are innovating how AI agents, not humans, access, retrieve, and reason over information available on the internet. As AI increasingly becomes the primary interface for web interaction, this pivotal layer is set to transform the function of conventional search engines.
This role involves tackling retrieval and search challenges within entirely new access patterns and scales.
Depending on your experience and the responsibilities you take on, this position can be classified at either the Staff or Principal level, granting you ownership over vital aspects of our applied AI research trajectory.
You will lead applied research initiatives that directly enhance how AI systems retrieve, ground, and utilize real-world information in production, ensuring that research is closely linked to large-scale deployment.
Your Responsibilities
Engage in projects at the intersection of search, retrieval, and LLM-based systems, shaping how AI agents engage with the web. This includes designing agent-native retrieval systems (distinct from human search UX), developing systems where LLMs actively query, iterate, and reason over results, and collaborating with cross-functional teams to foster innovation.
