Forward Deployed Engineer, Ecosystem

Details

Nebius builds infrastructure for demanding AI workloads — GPU clusters, inference runtimes, agent development environments, and data pipelines — and is creating the ecosystem function to ensure top AI companies build on and integrate with the platform.

Responsibilities

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 focused on what works at scale and in production.
• Scope partner architectures against the Nebius platform: identify where products snap together and where they break.
• Build production-quality proofs-of-concept across the AI stack including agentic pipelines, RAG architectures, inference optimization patterns, and multi-model orchestration.
• Produce working proofs-of-concept that serve as the starting point for product creation.
• Maintain a library of reference architectures and integration patterns for internal teams.

Technical Partner Scoping

• Work directly with partner engineering teams to scope, prototype, and progress integrations.
• Assess partner architectures honestly and report integration difficulty and feasibility.
• Provide technical guidance to partners on maximizing performance, reliability, and cost efficiency on Nebius infrastructure.
• Produce technical scoping that gives partners 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 solutions are ready.
• Surface recurring architectural patterns and integration gaps to inform the platform roadmap.
• Participate in platform planning as the technical voice of field integrations.

Ecosystem Presence

• Represent Nebius at hackathons, open-source communities, and 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 and understand recent releases and their implications for the stack.

Platform focus areas

Depending on background and fit, focus areas include one or more of:

• 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.

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 experience.
• 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 and ship meaningful things under time pressure.
• Experience building integrations across APIs and developer platforms and understanding where complexity lives.
• Comfortable working across external partner engineering teams and internal product and engineering teams simultaneously.
• Strong technical communication skills: able to explain architecture decisions and integration findings to both technical and non-technical audiences.

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).
• Recent success at major AI hackathons.
• Experience as a developer advocate, solutions engineer, or technical partner manager at a leading AI platform or developer tooling company.
• Early-engineer experience at a YC-backed AI startup.
• 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

Benefits

• 100% company-paid medical, dental, and vision coverage for employees and families.
• 401(k) plan with up to 4% company match and 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.
• Company-paid short-term, long-term, and life insurance coverage.

Compensation

We offer competitive salaries ranging from $255,000 - $315,000 OTE and equity based on experience, skills, and location. A stated compensation range in the posting: $255,000 — $315,000 USD.

Equal opportunity & work authorization

Nebius is an equal opportunity employer committed to fostering an inclusive workplace. 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.