[All remote jobs](https://jobicy.com/jobs.md)[![Nebius logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/06/d90c0566-221.webp)](https://jobicy.com/company/nebius.md)[Nebius](https://jobicy.com/company/nebius.md) # Senior Sales Engineer – Token Factory Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/nebius.md)ShareRemote from[Europe](https://jobicy.com/job-region/europe.md)SalaryUndisclosedDepartment[Sales](https://jobicy.com/categories/seller.md)EmploymentFull TimeExperienceSeniorPublished28 Oct 2026Apply before27 Nov 2026Listing views50Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Nebius is hiring a senior sales engineer to guide technically complex AI-inference opportunities from discovery through production feasibility. The role combines customer-facing solution architecture with commercial deal support, PoC scoping, and close collaboration with product and engineering teams. The successful candidate will evaluate LLM workloads, GPU economics, latency, throughput, and infrastructure trade-offs while preventing unscalable commitments. Strong hands-on knowledge of inference frameworks, cloud platforms, containers, and Kubernetes is expected. This is a remote Europe role focused on developer-native customers and high-performance AI infrastructure. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a highly senior, cross-functional position requiring deep AI-inference expertise alongside strong commercial judgment. The engineer must independently make and communicate consequential architecture and Go/No-Go recommendations for strategic customer opportunities. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$175,000US market range (per year)$140k–$220k0$242.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The annual USD figures are estimated market compensation for a senior AI infrastructure sales engineer; the yearly period is inferred from the senior full-time role and standard professional compensation conventions. ## Core skills Skills and capabilities most closely associated with this opportunity. [AI inference systems](https://jobicy.com/jobs?search_keywords=AI%20inference%20systems.md)[LLM workloads](https://jobicy.com/jobs?search_keywords=LLM%20workloads.md)[GPU infrastructure](https://jobicy.com/jobs?search_keywords=GPU%20infrastructure.md)[Solution architecture](https://jobicy.com/jobs?search_keywords=Solution%20architecture.md)[Technical discovery](https://jobicy.com/jobs?search_keywords=Technical%20discovery.md)[Sales engineering](https://jobicy.com/jobs?search_keywords=Sales%20engineering.md)[vLLM](https://jobicy.com/jobs?search_keywords=vLLM.md)[SGLang](https://jobicy.com/jobs?search_keywords=SGLang.md)[TensorRT-LLM](https://jobicy.com/jobs?search_keywords=TensorRT-LLM.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[Cloud platforms](https://jobicy.com/jobs?search_keywords=Cloud%20platforms.md)[Performance optimization](https://jobicy.com/jobs?search_keywords=Performance%20optimization.md)[PoC validation](https://jobicy.com/jobs?search_keywords=PoC%20validation.md) Sample interview questionsHow would you assess whether an LLM inference workload is ready for a production proof of concept?I would first establish measurable requirements for latency, TTFT, throughput, reliability, model behavior, traffic shape, and cost. I would then validate model compatibility, GPU and memory needs, integration dependencies, expected optimization work, and success criteria before agreeing to a tightly scoped PoC. How do you explain latency, throughput, and cost trade-offs to a customer and an account executive? I translate the technical metrics into the customer’s business outcome and make assumptions explicit. For example, I compare architecture options using expected request volume, concurrency, response-time targets, GPU utilization, and projected unit cost, then recommend the option that meets the agreed production objective without unnecessary customization. Describe how you would prevent an exploratory customer request from becoming unplanned engineering work. I would document the requested outcome, define technical boundaries and measurable acceptance criteria, estimate the level of customization required, and identify reusable versus one-off work. If the request is not economically or strategically viable, I would present alternatives or recommend a No-Go decision early. What factors would you examine when selecting an inference framework such as vLLM, SGLang, or TensorRT-LLM? I would assess model compatibility, batching behavior, KV-cache management, hardware support, latency and throughput targets, quantization options, operational maturity, observability, and deployment constraints. The final decision should balance benchmark performance with reliability and the customer’s production integration needs. How would you share recurring customer workload patterns with product and engineering teams? I would turn discovery findings into structured evidence, including workload type, model size, traffic characteristics, performance bottlenecks, requested optimizations, commercial impact, and frequency across accounts. This helps teams prioritize platform capabilities that solve repeatable customer needs rather than isolated deal-specific requests. Opportunity details ## About this role. 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 remotely from 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 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 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. Show more [Apply now >](https://jobicy.com/jobs/157507-senior-sales-engineer-token-factory.md) > Annual salary information is not provided for this position. 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