Senior Sales Engineer - Token Factory

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:

Customer trust at scale

You’re welcome to work remotely from Europe.

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

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.)

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

Comfort challenging assumptions and pushing back constructively

Commercial awareness – you understand that engineering time is a strategic resource

Programming Languages– Python

Frameworks and Libraries– vLLM, SGLang, TensorRT-LLM, OpenAI/Anthropic SDKs