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 Lead Sales Engineer 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 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

Cross-Functional Leadership

• 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 across teams
• Establish feedback loops that allow Product and Engineering to understand emerging customer demand
• Help leadership make informed tradeoffs between revenue opportunity, customer impact, and engineering investment

We expect you to have:

• Deep understanding of AI inference systems and GPU-backed infrastructure
• Significant experience with LLM workloads and performance-sensitive environments
• Experience with inference frameworks and libraries such as vLLM, SGLang, and TensorRT-LLM
• Strong ability to reason about latency, throughput, GPU utilization, cost, and architecture tradeoffs
• Experience leading, mentoring, or managing Sales Engineers, Solution Architects, or similar customer-facing technical teams
• Demonstrated success supporting complex enterprise or developer-focused technical sales cycles
• Strong customer presence with engineering-first organizations
• Ability to operate credibly with technical founders, engineering leaders, and senior customer stakeholders
• Strong judgment around when to standardize, when to customize, and when to say no
• Comfort challenging assumptions and pushing back constructively with both customers and internal stakeholders
• Commercial awareness - you understand that engineering time and GPU capacity are strategic resources
• Ability to move between detailed technical discussions, deal strategy, team leadership, and executive communication
• Experience building repeatable processes and technical standards in a fast-growing organization

What success looks like:

• The Sales Engineering team operates with clear standards, ownership, and technical rigor
• Sales Engineering capacity is allocated predictably toward the highest-value opportunities
• Strategic deals are technically sound before significant Engineering engagement
• PoCs are consistently scoped, measurable, and economically justified
• Technical risks and misaligned customer expectations are identified early
• PoC-to-production conversion improves
• Engineering teams spend less time on poorly qualified or one-off customer requirements
• Repeatable customer patterns systematically influence Product and Engineering priorities
• Sales has a trusted technical partner for navigating complex AI infrastructure opportunities
• Customers view the Sales Engineering organization as trusted architectural advisors
• Sales Engineers grow in technical depth, commercial judgment, and customer leadership

Pay Transparency

We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.

Base Compensation Range

$228,000-$285,000 USD

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