Lead Sales Engineer - 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 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 

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.