Lead Sales Engineer - Token Factory

Nebius

5h•$228,000 - $285,000•Remote

About The Position

Nebius is 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.

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 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

Responsibilities

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 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