Head of Sales Engineering - Token Factory

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 always guarantee production success. Our Sales Engineering team exists to ensure that what we commit to with customers is scalable, efficient, economically viable, and aligned with platform strategy.  We are looking for a Head of Sales Engineering to lead and develop our Sales Engineering function while remaining deeply involved in our most strategic and technically complex customer engagements. This is a player-coach role . You will lead a team of Sales Engineers, establish the technical standards and operating mechanisms for customer engagements, and personally provide architectural leadership on high-impact opportunities. You will operate at the intersection of customer ambition, engineering reality, and commercial growth, ensuring the team consistently influences: Revenue quality Engineering focus and capacity Product evolution PoC-to-production conversion Customer trust at scale You’re welcome to work remotely from the United States . Your responsibilities will include:   Team Leadership & Development Lead, coach, and develop a high-performing team of Sales Engineers Set clear expectations for technical quality, customer engagement, and commercial impact Provide hands-on technical mentorship and support the growth of individual team members Establish consistent approaches to discovery, architecture reviews, PoC qualification, and production readiness Allocate Sales Engineering capacity across opportunities based on strategic value, technical complexity, and probability of success Create an environment where the team can challenge assumptions, escalate risks early, and make high-quality technical decisions Support hiring, onboarding, and development of the Sales Engineering organization as the business scales Strategic Technical Leadership Act as the senior technical advisor on strategic and complex customer opportunities Lead deep technical discovery with engineering teams, technical founders, and customer executives Guide the team in understanding model requirements, traffic expectations, latency constraints, GPU economics, and system dependencies Translate customer ambition into production-feasible architectures Identify hidden technical, operational, and economic risks before significant resources are committed Step directly into critical opportunities when additional technical depth or leadership is required Commercial Acceleration & Deal Governance Partner closely with Sales leadership on strategic deals, account planning, and technical qualification Influence deal strategy through architectural clarity and a strong understanding of customer requirements Establish clear technical qualification and escalation mechanisms for complex opportunities Ensure customer commitments are aligned with current or strategically planned platform capabilities Prevent misaligned commitments before Engineering resources are allocated Improve PoC-to-production conversion by ensuring technical and economic realism from the beginning Help Sales and Sales Engineering balance customer urgency with sustainable platform development PoC Architecture & Validation Establish standards for how the team scopes, designs, and evaluates customer PoCs Ensure measurable success criteria are defined, including latency, TTFT, throughput, reliability, and cost envelope Guide workload classification and determine the appropriate depth of optimization Align the right resources across Sales Engineering, ML Solution Architecture, Product, Engineering, and GPU capacity Drive structured Go / No-Go decisions for complex engagements Prevent uncontrolled customization, hidden R&D, and poorly scoped engineering commitments Ensure successful PoCs have a clear and realistic path to production Pattern Recognition & Platform Leverage Build a systematic view of technical patterns emerging across customer engagements Identify recurring workload, configuration, and architecture patterns Quantify demand for advanced optimi