Forward Deployed Engineer, Ecosystem

Nebius is leading a new era in cloud computing to serve the global AI economy. As a Forward Deployed Engineer, Ecosystem, you will design and prototype integrations between partner products and the Nebius platform, ensuring optimal performance and reliability while translating findings into actionable product requirements.

Responsibilities:

• Design and prototype integrations between partner products and the Nebius platform — fast, hands-on, and technically sound
• Define reference architectures for partner integrations — not just what works, but how it should work at scale and in production
• Scope partner architectures against our platform — how does this product actually work on our stack, where does it snap together, where does it break
• Build production-quality proof-of-concepts across the AI stack including agentic pipelines, RAG architectures, inference optimization patterns, and multi-model orchestration
• Produce working proof-of-concepts that serve as the starting point for product creation — not a requirements doc, a working thing
• Maintain a library of reference architectures and integration patterns that internal product and engineering teams can build from
• Work directly with partner engineering teams to scope, prototype, and progress integrations
• Assess partner architectures honestly — if the integration is painful, that is signal; if it snaps together in a weekend, that is also signal; report both
• Provide technical guidance to partners on how to maximize performance, reliability, and cost efficiency on Nebius infrastructure
• Produce technical scoping that gives your pod partner and internal teams a clear picture of integration feasibility, depth, and complexity
• Translate external integration findings into actionable product requirements for Nebius platform teams
• Work with ISV partners, SI teams, and field teams to scale solution adoption and drive revenue once a solution is ready
• Surface recurring architectural patterns and integration gaps to inform platform roadmap decisions
• Participate in platform planning as the technical voice of what you are seeing and building in the field
• 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

Requirements:

• 6+ years of hands-on engineering experience in AI application development, ML systems, or AI infrastructure
• Deep working knowledge of the AI developer stack — LLM APIs, inference runtimes, orchestration frameworks, vector databases, RAG architectures, 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
• Comfortable working across both 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
• Experience with inference frameworks and optimization: vLLM, SGLang, TensorRT-LLM, speculative decoding, quantization, batching, 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, Git