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Nebius is building a full-stack AI cloud platform for the global AI economy, supporting developers and enterprises from data and model training through production deployment. The company is Nasdaq-listed, headquartered in Amsterdam, with 1,500+ employees and R&D hubs across Europe, UK, North America, and Israel.
This Senior Sales Engineer role is foundational to Nebius's high-performance AI inference platform for developer-native teams running latency- and cost-sensitive workloads at scale. You will operate at the intersection of customer ambition, engineering reality, and commercial growth, serving as a technical partner to customers and force multiplier for Sales and Engineering teams.
Key responsibilities include:
**Strategic Technical Discovery:** Lead deep technical discovery with engineering teams and technical founders. Understand model requirements, traffic expectations, latency constraints, GPU economics, and system dependencies. Translate customer ambition into production-feasible architectures and identify hidden technical risks early.
**Commercial Acceleration:** Partner tightly with Sales on strategic deals. Influence deal strategy through architectural clarity. Prevent misaligned commitments before engineering allocation. Increase PoC-to-production conversion by ensuring technical realism.
**PoC Architecture & Validation:** Define measurable success criteria (latency, TTFT, throughput, cost envelope). Classify workload complexity and required optimization depth. Align appropriate resources (ML Solution Architects, engineering, GPU capacity). Drive structured Go/No-Go decisions and prevent uncontrolled customization or hidden R&D.
**Pattern Recognition & Platform Leverage:** Identify recurring configuration patterns across customers. Quantify demand for advanced optimizations (quantization, speculative decoding, etc.). Surface structured insights to Product and Engineering. Help evolve platform capabilities based on real workload data.
Success looks like: strategic deals are technically sound before engineering engagement; PoCs are clearly scoped and economically justified; engineering capacity is allocated predictably; conversion to production improves; customers view you as a trusted architectural advisor.
**Requirements:**
- Deep understanding of AI inference systems and GPU-backed infrastructure
- Experience with LLM workloads and performance-sensitive environments
- Experience with inference frameworks and libraries (e.g., vLLM, SGLang, TensorRT-LLM)
- Ability to reason about latency, throughput, cost, and architecture tradeoffs
- Strong customer presence with engineering-first organizations
- Comfort challenging assumptions and pushing back constructively
- Commercial awareness – understanding that engineering time is a strategic resource
**Preferred Technical Stack:**
- Programming Languages: Python
- Frameworks and Libraries: vLLM, SGLang, TensorRT-LLM, OpenAI/Anthropic SDKs
- Frameworks for Agentic Pipelines: Langchain, Langsmith, smolagents, or equivalent
- API and Web Frameworks: FastAPI, Flask
- MLOps and DevOps tools: Kubernetes (K8s), Docker, Git
- Cloud Platforms: AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)
