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.
The Product
In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible.
We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale.
The Role
We are looking for a Senior AI Software Engineer to build the agents that sit on top of our search platform: systems that take a user's intent, break it into steps, gather and verify information from the web, and return answers an agent can act on. You will work across applied research and engineering, designing how agents plan, call tools, retrieve, and reason so they accomplish open-ended tasks reliably and at scale.
This is a high-ownership role at the intersection of agent systems, retrieval, and product. You will turn frontier-model capabilities into dependable, production-grade agent behavior, and you will own that behavior end to end, from the prompts and tools to the evaluation that proves it works.
In this position, your responsibility will be to
Design and build AI agents that plan, retrieve, and reason over real-world information to complete open-ended tasks
Build the tool interfaces and context engineering that let frontier models use our search and other tools effectively
Mine and analyze usage data to build agents that learn and improve continually from how they are used
Turn new model capabilities into reliable product features, and own them from prototype to production
Define the evaluations, metrics, and guardrails that prove an agent is accurate, grounded, and safe
Improve agent quality across reasoning, planning, tool use, and grounding against real user tasks
Build the backend and infrastructure that run agents reliably under high volume
Collaborate with the search, ML, and product teams to make agent and platform capabilities reinforce each other
You may be a good fit if you:
6+ years of software engineering experience, with a track record of shipping complex systems to production
Strong understanding of LLMs and transformer architecture, and how model behavior shapes what agents can do
Able to mine and analyze data to build agents that learn and improve continually
Hands-on experience building agentic systems: tool calling, planning, multi-step or long-running task execution
Experience with agentic frameworks (e.g. LangChain, DeepAgents) and tracing tools (e.g. LangSmith)
Strong grasp of context engineering and tool interfaces for frontier LLMs
Comfortable defining the metrics and evaluations that prove a system works, and iterating on them
Strong product judgment; you turn vague needs into reliable systems and ship without waiting for perfect specs
Thrive in a small, fast-moving team and take ownership end to end
Strong candidates may also have experience with:
Retrieval-augmented generation, search, or information-retrieval systems
Post-training, reinforcement learning, or fine-tuning for reasoning or tool use
Building developer platforms or reusable primitives (SDKs, tool/plugin systems, workflow engines)
Evaluation, benchmarking, or quality systems for LLM-powered products
Time at a fast-growing startup or on a high-ownership engineering team
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.
Skills Required
- 6+ years of software engineering experience, with a track record of shipping complex systems to production
- Strong understanding of LLMs and transformer architecture
- Hands-on experience building agentic systems: tool calling, planning, multi-step or long-running task execution
- Experience with agentic frameworks (e.g., LangChain, DeepAgents) and tracing tools (e.g., LangSmith)
- Able to mine and analyze usage data to build agents that learn and improve continually
- Strong grasp of context engineering and tool interfaces for frontier LLMs
- Comfortable defining metrics, evaluations, and guardrails for agent accuracy, grounding, and safety
- Experience building backend and infrastructure to run agents reliably under high volume
- Strong product judgment and ability to take ownership end to end
- Experience with retrieval-augmented generation, search, or information-retrieval systems
- Experience with post-training, reinforcement learning, or fine-tuning for reasoning or tool use
- Experience building developer platforms or reusable primitives (SDKs, tool/plugin systems, workflow engines)
- Experience with evaluation, benchmarking, or quality systems for LLM-powered products
- Experience at a fast-growing startup or high-ownership engineering team
What We Do
Cloud platform specifically designed to train AI models
