Lead Sales Engineer – Token Factory

Emploi pas sur LinkedIn

🕒 il y a 16 jours

🇺🇸 États-Unis – Télétravail

💵 $228 000 - $285 000 / an

⏰ Temps Plein

🟠 Senior

💸 Ingénieur technico-commercial

👻 Score fantôme 20%

Ce que ça veut dire : la probabilité que cette offre reste ouverte sans recrutement actif.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Nebius Group

Site WebLinkedInTous les Emplois

1001 - 5000 employés

🤖 Intelligence artificielle

🏢 Entreprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Le groupe Nebius construit l’une des principales entreprises d’infrastructure AI au monde, en se concentrant sur la fourniture de la puissance de calcul, du stockage et des outils nécessaires aux développeurs dans le domaine de l’AI. Basée en Europe et cotée au Nasdaq, Nebius dispose d’une présence mondiale avec des centres de R&D en Europe, en Amérique du Nord et en Israël. L’offre principale de l’entreprise est une plateforme cloud centrée sur l’AI, conçue pour des workloads AI intensifs, complétée par diverses autres activités impliquées dans le développement de l’AI générative, l’edtech et les technologies autonomes.

Description

• Lead, coach, and develop a high-performing team of Sales Engineers • Set standards for technical quality, customer engagement, and commercial impact • Establish consistent approaches to discovery, architecture reviews, PoC qualification, and production readiness • Allocate Sales Engineering capacity across strategic opportunities • Support hiring, onboarding, and organizational development • Act as senior technical advisor on strategic and complex customer opportunities • Lead technical discovery with engineering teams, technical founders, and customer executives • Translate customer needs into production-feasible architectures • Identify technical, operational, and economic risks • Partner with Sales leadership on strategic deals, account planning, and technical qualification • Govern technical qualification, escalation, customer commitments, and deal strategy • Improve PoC-to-production conversion • Establish standards and measurable success criteria for customer PoCs, including latency, TTFT, throughput, reliability, and cost • Align resources across Sales Engineering, ML Solution Architecture, Product, Engineering, and GPU capacity • Drive structured Go / No-Go decisions and prevent uncontrolled customization • Identify recurring workload, configuration, and architecture patterns • Quantify demand for quantization, speculative decoding, and other inference techniques • Surface customer insights to Product and Engineering leadership • Influence platform priorities using workload data and commercial opportunity • Serve as an interface between Sales, Sales Engineering, Product, and Engineering • Improve communication of technical decisions, risks, and dependencies • Help leadership balance revenue opportunity, customer impact, and engineering investment

🎯 Exigences

• 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 – 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 • Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility

🏖️ Avantages

• 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