Staff Applied AI Researcher (Agentic Search)
关于尼比斯：尼比斯正在为全球人工智能经济带领云基础设施的新时代。我们正在建立一个完整的 AI 云平台，支持开发人员和企业从数据和模型培训到生产部署，而无需建造大型内部 AI/ML 基础设施的成本和复杂性。由工程师们为工程师构建。从大规模 GPU 编排到推断优化，我们拥有计算，存储，网络和应用 AI 方面的艰难问题。我们在纳斯达克 (NBIS) 上上市并总部位于阿姆斯特丹，我们有全球足迹，遍布欧洲，英国，北美和以色列各地的研发中心。我们的1500多名工程师团队包括数百名拥有硬件，软件和人工智能研发方面的深入专业知识。我们正在寻找一名员工应用人工智能研究人员加入一个快速增长的团队建立代理本土搜索平台 - - 对于 AI 系统的网络访问层。你可以把这看作是 Google 为 AI 代理：是一个针对机器而不是人类设计的系统。我们正在构建代理搜索，AI 系统积极计划，检索，评估和精炼信息而不仅仅返回结果。当 AI 成为网页的主要接口时，这个层将取代传统搜索引擎的作用。我们正在设计如何人工智能代理人 - 而不是人类 - 在严格的延迟和可靠性限制下，实时获取，评估和推理网络数据。这意味着在完全新的访问模式下解决检索和排名并以显著规模进行处理，系统运行不断变化的，不结构化的数据，并在24次7次服务上提供数万个生产工作负载。这个角色伴随着我们应用人工智能研究方向和系统设计的关键部分的所有权，有强烈的期望定义新方法和出货量测量的影响。你将的工作内容：· 设计针对机器消费而非人类搜索优化的代理本土检索系统 · 构建 LLM 反复规划，查询，完善和推理结果的系统 · 在现实世界中限制下开发多步骤的排名和检索方法；经纪人驱动的工作流程你的责任：· 在检索和排名系统中推动应用研究和技术方向 · 设计和发展多阶段检索架构 （查询理解，重写，重新排名，反复检索）· 在实时网络数据上制定基于 LLM 的方法 · 定义和实施
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About Nebius:
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.
We are seeking a Staff Applied AI Researcher to join a fast growing team building an agent native search platform - the web access layer for AI systems.
You can think of this as Google for AI agents: a system designed for machines, not humans. We are building agentic search, where AI systems actively plan, retrieve, evaluate, and refine information rather than simply returning results. As AI becomes the primary interface to the web, this layer will replace the role of traditional search engines.
We are designing how AI agents - not humans - retrieve, evaluate, and reason over web data in real time, under strict latency and reliability constraints. This means solving retrieval and ranking under entirely new access patterns and at significant scale, with systems operating over constantly changing, unstructured data and serving tens of thousands of production workloads 24 by 7.
This role comes with ownership over key parts of our applied AI research direction and system design, with a strong expectation of defining new approaches and shipping measurable impact in production.
What you'll work on:
- Designing agent native retrieval systems optimised for machine consumption rather than human search UX
- Building systems where LLMs iteratively plan, query, refine, and reason over results
- Developing ranking and retrieval approaches for multi step, agent driven workflows under real world constraints
Your responsibilites:
- Drive applied research and technical direction across retrieval and ranking systems
- Design and evolve multi stage retrieval architectures (query understanding, rewriting, reranking, iterative retrieval)
- Develop methods for grounding LLMs in real time web data at scale
- Define and implement new evaluation paradigms and metrics for agentic systems, where correctness is not reducible to clicks
- Lead experimentation on modern retrieval approaches (embeddings, hybrid search, reranking) and bring them into production
- Analyse trade-offs across relevance, latency, and cost at scale
- Work closely with engineering to deploy systems in high throughput, low latency environments
- Own ambiguous problems end to end and contribute to product and research direction
- Mentor engineers and help raise the technical bar of the team
Must haves:
- 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, recommendation systems, or assistants
- Strong understanding of modern deep learning, especially 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 and at least one of Go, C++, or similar
- Ability to operate in a fast moving, product driven environment with high ownership and autonomy
Nice to haves
- Experience with large scale search or recommendation systems
- Background in agentic AI systems (agents, tool use, autonomous workflows)
- Experience with RAG, multi step retrieval, or tool use
- Publications, open source, or similar signals of technical depth and impact
Benefits & Perks:
- 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
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.