Forward Deployment Engineering Manager

Nebius builds the infrastructure serious AI teams run on - GPU clusters, inference runtimes, agent development environments, data pipelines - all of it purpose-built for the most demanding AI workloads. What we are now building is the ecosystem function that ensures the best AI companies choose to build on us, integrate with us, and stay. As Manager of Forward Deployment Engineering, you will lead a team of FDEs who sit at the intersection of solution architecture and hands-on engineering. You will set the technical bar, develop your team's craft, and ensure the work your team ships - integrations, reference architectures, and proofs of concept - is production-quality and strategically sound. You will also stay technically engaged: reviewing architectures, unblocking hard scoping problems, and occasionally prototyping yourself when the situation calls for it. You're welcome to work remotely in the United States. Your responsibilities will include: People & Team Leadership - Hire, develop, and retain a team of Forward Deployed Engineers across ecosystem focus areas: agentic, inference, infrastructure, and data. - Set clear expectations for technical quality and partner engagement; coach engineers toward those standards. - Run structured 1:1s, provide direct and actionable feedback, and own the growth of each person on your team. - Build a team culture where moving fast and building well are not in tension. - Partner with Recruiting to define what great looks like for FDE roles and actively source candidates from the AI builder community. Technical Oversight & Architecture - Review and elevate the technical work your team produces - integration architectures, proofs of concept, reference patterns, and partner scoping assessments. - Serve as a technical escalation point for complex partner engagements; step in hands-on when needed. - Maintain a high bar for what goes into the reference architecture library - not just what works, but what should be emulated. - Stay current with the AI tooling ecosystem so you can guide your team's technical judgment, not just their output. Ecosystem Presence - Represent Nebius at hackathons, in open source communities, and at technical events. - Build in public - demos, reference architectures, and integrations that establish Nebius as the platform serious AI builders choose. - Stay current with the AI tooling ecosystem - you know what shipped last week and what it means for our stack. Platform focus areas Depending on your background and mutual fit, you will focus on one or more of the following: - Agentic - agent frameworks, memory systems, tool integration, orchestration, MCP, and guardrails - Managed Inference - inference runtimes, model serving, optimization tooling, speculative decoding, and KV-cache routing - IaaS / Managed Infrastructure - cloud-native integrations, GPU orchestration, and enterprise platform connectors - Data - vector databases, retrieval systems, RAG architectures, data pipeline integrations, and synthetic data tooling Partner & Internal Stakeholder Engagement - Engage directly with senior partner engineering leaders and founding CTOs; your team handles the working level, and you handle the strategic level. - Translate what your team is seeing in the field into actionable product requirements for Nebius platform teams. - Represent the FDE function in platform planning discussions as the technical voice of ecosystem integration. - Work with ISV, SI, and field teams to scale solution adoption and drive revenue once integrations are ready. We expect you to have: - 8+ years of hands-on engineering experience in AI application development, ML systems, or AI infrastructure. - 2+ years managing or leading a team of engineers, with a track record of developing technical talent. - Deep working knowledge of the AI developer stack - LLM APIs, inference runtimes, orchestration frameworks, vector databases, RAG architectures, and agentic pipelines - built through shipping, not reading. - Hands-on experience with agentic frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent. - Strong Python programming skills and comfort prototyping end-to-end AI systems quickly. - Experience defining reference architectures and technical patterns - not just implementing them. - Proven ability to move from idea to working prototype fast - you have shipped meaningful things under time pressure and found it energizing. - Experience building integrations across APIs and developer platforms - you understand where the complexity actually lives. - Comfort working across external partner engineering teams and internal Nebius Product and Engineering teams simultaneously. - Strong technical communication - you can explain architecture decisions and integration findings to a founding CTO and a non-technical partner lead in the same day.