Forward Deployed Engineer, Ecosystem at Nebius

United States

$255k - $315k

Why work at Nebius Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field. Where we work Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&D team. The role 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 a Forward Deployed Engineer, Ecosystem, you will sit at the intersection of solution architecture and hands-on engineering. You assess how partner products actually work on our stack, define the reference architecture for each integration, build the working prototype that proves it, and translate what you find into product requirements that shape what Nebius ships next. Your responsibilities will include: Solutioning & Architecture 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 Technical Partner Scoping 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 Internal 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 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, guardrails Managed Inference — inference runtimes, model serving, optimization tooling, speculative decoding, KV-cache routing IaaS / Managed Infrastructure — cloud-native integrations, GPU orchestration, enterprise platform connectors Data — vector databases, retrieval systems, RAG architectures, data pipeline integrations, synthetic data tooling We expect you to have: 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 It will be an added bonus if you have: 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 Preferred technical stack: Languages — Python ML frameworks — vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI / Anthropic SDKs Agentic frameworks — LangChain, LangGraph, CrewAI, AutoGen, smolagents, or equivalent Vector databases — Qdrant, Weaviate, Milvus, pgvector API and web frameworks — FastAPI, Flask DevOps — Kubernetes, Docker, Git Cloud platforms — AWS, GCP, 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. Compensation We offer competitive salaries, ranging from $255K - $315K OTE (On-Target Earnings) and equity based on your experience, skills, and location. If you’re up to the challenge and are excited about AI and ML as much as we are, join us! Compensation We offer competitive compensation packages based on experience. Compensation Range $255,000—$315,000 USD What we offer: Competitive salary and comprehensive benefits package. Opportunities for professional growth within Nebius. Flexible working arrangements. A dynamic and collaborative work environment that values initiative and innovation. We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us! 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.