Senior Software Engineer, Agents (Agentic Search) - Nebius

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 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 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 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

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 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 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 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

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 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 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 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