Staff Applied AI Researcher (Agentic Search)
Salary not listed • Remote • Amsterdam, Netherlands; London, United Kingdom; Remote - Europe; Zurich, Switzerland • Full-time
Posted 2mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Amsterdam, Netherlands; London, United Kingdom; Remote - Europe; Zurich, Switzerland
Tech Stack
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
AI Engineer
About the job
Nebius is building a full-stack AI cloud platform for the global AI economy, supporting developers and enterprises from data and model training through to production deployment. We are seeking a Staff Applied AI Researcher to join a fast-growing team building an agent-native search platform, designed as a web access layer for AI systems. This platform enables AI systems to actively plan, retrieve, evaluate, and refine information, aiming to replace traditional search engines as AI becomes the primary interface to the web. The role involves designing systems to search and reason over web data in real-time under strict latency and reliability constraints, solving retrieval, ranking, and passage extraction challenges at significant scale over constantly changing, unstructured data. This position offers ownership over key aspects of applied AI research direction and system design, with a strong expectation of defining new approaches and delivering measurable impact in production.
Responsibilities
- Drive applied research and technical direction for retrieval and ranking systems.
- Design and evolve multi-stage retrieval architectures, including query understanding, rewriting, retrieval, reranking, and iterative retrieval.
- Lead experimentation on modern retrieval approaches such as embedding research, sparse and hybrid retrieval, reranking, and context compression, and implement them in production.
- Develop methods for grounding LLMs in real-time web data at scale, utilizing trust, quality, and freshness signals to surface reliable sources.
- Partner with the evaluation team to define new metrics for agentic systems where correctness is not solely based on clicks.
- Analyze trade-offs between relevance, latency, and cost, and collaborate with engineering to deploy web-scale systems.
- Mentor researchers and engineers to elevate the team's technical capabilities.
Requirements
- 8+ years of experience in applied AI, ML, or software engineering.
- Proven track record of shipping ML or AI systems to production at scale.
- Deep experience with search, retrieval, ranking, or building RAG-based assistants.
- Strong understanding of modern deep learning, particularly transformers and embeddings.
- Experience with LLM-integrated or knowledge-intensive systems.
- Experience designing evaluation frameworks and metrics for ML systems.
- Strong programming skills in Python.
- Top-tier publications in relevant AI conferences (e.g., ICLR, ICML, NeurIPS, AAAI, EMNLP, ACL).
- Ability to operate autonomously in a fast-moving, product-driven environment with high ownership.
- Background in agentic AI systems (agents, tool use, autonomous workflows) is a plus.
- Experience with RL post-training of LLMs or VLMs is a plus.
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