Lead Sales Engineer - Token Factory

Details

Nebius is building a full-stack AI cloud platform for developers and enterprises, supporting workloads from data and model training through production deployment. The role focuses on a high-performance AI inference platform for developer-native teams running latency- and cost-sensitive workloads at scale.

This is a player-coach position responsible for leading and developing the Sales Engineering function while providing architectural leadership on strategic and technically complex customer engagements. The role is remote within the United States.

Responsibilities

Team Leadership and Development

• Lead, coach, and develop a high-performing team of Sales Engineers.
• Set expectations for technical quality, customer engagement, and commercial impact.
• Provide hands-on technical mentorship and support individual team members' growth.
• Establish consistent approaches to discovery, architecture reviews, proof-of-concept qualification, and production readiness.
• Allocate Sales Engineering capacity 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.

Strategic Technical Leadership

• Act as the senior technical advisor on strategic and complex customer opportunities.
• Lead 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 requirements into production-feasible architectures.
• Identify technical, operational, and economic risks before significant resources are committed.
• Provide additional technical depth and leadership for critical opportunities.

Commercial Acceleration and Deal Governance

• Partner with Sales leadership on strategic deals, account planning, and technical qualification.
• Influence deal strategy through architectural clarity and understanding of customer requirements.
• Establish technical qualification and escalation mechanisms for complex opportunities.
• Ensure customer commitments align with current or strategically planned platform capabilities.
• Prevent misaligned commitments before Engineering resources are allocated.
• Improve proof-of-concept-to-production conversion through technical and economic realism.
• Help Sales and Sales Engineering balance customer urgency with sustainable platform development.

Proof-of-Concept Architecture and Validation

• Establish standards for scoping, designing, and evaluating customer proof-of-concepts.
• Define measurable success criteria, including latency, time to first token (TTFT), throughput, reliability, and cost envelope.
• Guide workload classification and determine the appropriate depth of optimization.
• Align 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 research and development, and poorly scoped engineering commitments.
• Ensure successful proof-of-concepts have a clear and realistic path to production.

Pattern Recognition and Platform Leverage

• Build a systematic view of technical patterns across customer engagements.
• Identify recurring workload, configuration, and architecture patterns.
• Quantify demand for optimizations such as quantization, speculative decoding, and other inference techniques.
• Provide structured customer insights and technical evidence to Product and Engineering leadership.
• Distinguish repeatable platform requirements from one-off customer requests.
• Influence platform priorities using workload data and commercial opportunity.
• Turn successful customer architectures and lessons learned into reusable patterns.

Cross-Functional Leadership

• Serve as an interface between Sales, Sales Engineering, Product, and Engineering.
• Represent customer technical requirements while maintaining awareness of platform strategy and engineering constraints.
• Improve communication of technical decisions, risks, and dependencies.
• Establish feedback loops for Product and Engineering to understand emerging customer demand.
• Help leadership balance revenue opportunity, customer impact, and engineering investment.

Requirements

• 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 about when to standardize, customize, or decline an engagement.
• Ability to challenge assumptions and push back constructively with customers and internal stakeholders.
• Commercial awareness, including understanding 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.

Success Measures

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

Benefits

• Base compensation range: $228,000–$285,000 USD per year.
• Competitive compensation and benefits.
• Career growth and learning opportunities.
• Flexibility and ownership.
• Collaborative and innovative culture.
• Opportunity to work on impactful AI projects.
• International environment and talented teams.

Nebius is an equal opportunity employer committed to an inclusive and diverse workplace. Applicants must be authorized to work in the country in which they apply and must provide proof of employment eligibility as a condition of hire.