Senior Sales Engineer - Token Factory

About Nebius:Nebius is leading a new era in cloud infrastructure for the global AI economy. 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 We are building a high-performance AI inference platform for developer-native teams running latency- and cost-sensitive workloads at scale. In AI infrastructure, PoC success does not guarantee production success. This role ensures that what we commit to is scalable, efficient, and aligned with platform strategy. We are looking for a Senior Sales Engineer to become a foundational technical partner to our customers and a force multiplier for Sales and Engineering. You will shape complex AI workloads from first discovery through production feasibility validation, ensuring technical rigor, economic viability, and scalable architecture decisions. You will operate at the intersection of customer ambition, engineering reality, and commercial growth, influencing: Revenue quality Engineering focus Product evolution Customer trust at scale You’re welcome to work remotelyfrom Europe. Your responsibilities will 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 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, etc.) Drive structured Go / No-Go decisions 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 We expect you to have: 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 – you understand 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 / 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) What success looks like: Strategic deals are technically sound before engin