Note: The job is a remote job and is open to candidates in USA. Nebius is building a full-stack AI cloud platform for developers and enterprises, supporting workloads from data and model training through production deployment. The Lead Sales Engineer will lead and develop the Sales Engineering function while providing architectural leadership on strategic customer engagements, ensuring scalable, economically viable solutions and strong PoC-to-production conversion.
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
Skills
- 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
- You're welcome to work remotely from the United States
- Applicants must be authorized to work in the country in which they apply
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
Company Overview