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're building the infrastructure layer for agentic web interaction at scale. Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help developers build agents that are not only intelligent — but also informed.
We work with some of the most innovative teams in AI — from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.
Managing Kubernetes clusters across multiple environments and regions
Owning infrastructure as code for all resources
Maintaining and improving CI/CD pipelines and GitOps-based deployments
Maintaining and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processors
Building out monitoring, alerting, and observability
Debugging production issues across services
Managing cloud costs and capacity planning
Working closely with a small engineering team — you'd own infra, not a slice of it
~3+ years in a DevOps or platform engineering role, working in production environments
Proven experience designing and operating large-scale, distributed systems, with a solid understanding of API design, reliability, and performance at scale
Strong Kubernetes experience in a managed cloud environment
Proficiency with infrastructure as code (Terraform or similar)
Experience with GitOps-based deployment workflows
Built or maintained observability stacks (logging, metrics, alerting)
Experience handling production incidents calmly and methodically
Multi-region deployments
Search infrastructure
Data pipeline experience (streaming, warehousing)
Proxy/networking infrastructure at scale
Full ownership — small team, you own the entire infrastructure, not a slice of it
Real scaling challenges — bursty scraping workloads, cache invalidation, multi-region, millions of daily requests
AI-native company — your infra directly powers AI agents used by leading companies in the space.
Benefits & Perks:
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.
The Nebius AI Cloud brings powerful full-stack infrastructure for AI developers and practitioners across startups, enterprises and science institutes to build and deploy generative AI applications and rapidly deliver scientific breakthroughs by training and running ML models within a secure, high-performance, and cost-optimized cloud environment.
