Job description
About Nebius:
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.
Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
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.
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.
Base Compensation Range
$208,800—$261,000 USD
Benefits & Perks:
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
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
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.
If you need accommodations during the application process, please let us know.
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.