Forward Deployment Engineering Manager

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. It will be an added bonus if you have Experience with inference frameworks and optimization: vLLM, SGLang, TensorRT-LLM, speculative decoding, quantization, batching, and KV-cache routing. Familiarity with NVIDIA's software stack: CUDA, TensorRT, NeMo, or equivalent. Experience with multimodal AI models - vision-language, speech, or structured data. Won or placed at major AI hackathons in the past 12 months. Worked as a developer advocate, solutions engineer, or technical partner manager at a leading AI platform or developer tooling company. Been an early engineer at a YC-backed AI startup - you built the product under real constraints. Open source projects or public demos with meaningful community adoption. Proficiency with DevOps tools: Docker, Kubernetes, and Git. Preferred technical stack Languages - Python ML frameworks - vLLM, SGLang, TensorRT-LLM, Transformers, and OpenAI / Anthropic SDKs Agentic frameworks - LangChain, LangGraph, CrewAI, AutoGen, smolagents, or equivalent Vector databases - Qdrant, Weaviate, Milvus, and pgvector API and web frameworks - FastAPI and Flask DevOps - Kubernetes, Docker, and Git Cloud platforms - AWS, GCP, and Azure Key Employee Benefits Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) Plan: Up to 4% company match with immediate vesting. Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote Work Reimbursement: Up to $85/month for mobile and internet. Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Starting Base Compensation Range: $225,800 - $281,000 USD Benefits & Perks Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impact