Sales Engineer

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.

• 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.
• 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.
• 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.
• 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.
• Build a systematic view of technical patterns emerging across customer engagements.
• Identify recurring workload, configuration, and architecture patterns.
• Quantify demand for advanced optimizations such as quantization, speculative decoding, and other inference techniques.
• Surface structured customer insights and technical evidence to Product and Engineering leadership.
• Help distinguish repeatable platform requirements from one-off customer requests.
• Influence platform priorities based on real workload data and commercial opportunity.
• Turn successful customer architectures and lessons learned into reusable patterns for the wider Sales Engineering organization.
• Serve as a key interface between Sales, Sales Engineering, Product, and Engineering.
• Represent customer technical requirements while maintaining a clear view of platform strategy and engineering constraints.
• Improve how technical decisions, risks, and dependencies are communicated.