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Salary: USD 275,000 - 350,000 / annual
Dust is hiring a Global Head of Solutions Engineering to build and lead the technical customer-facing organization for the next stage of growth. This is a founding player-coach role where you will remain directly involved in the most important customer engagements while significantly scaling the team and building repeatable motions.
You will lead the global Solutions Engineering function across pre-sales and post-sales, owning how Dust earns technical wins during complex enterprise evaluations and how the company deepens value, adoption, and stickiness during and after deployment. You will have authority over organization structure, hiring, performance, career paths, technical standards, coverage, resource allocation, and operating model. As a member of Dust's GTM leadership group, you will work closely with Sales, Customer Success, AI Deployment, Product, and Engineering.
Key responsibilities include:
- Define the global vision, strategy, and operating model for Solutions Engineering; design team structure, leadership model, roles, career paths, and coverage model across Paris, New York, San Francisco, and London.
- Lead hiring, development, and management of Solutions Engineers and future SE leaders with a high talent bar combining technical depth, business judgment, executive presence, and customer empathy.
- Own the technical win by defining how Dust qualifies, scopes, and executes complex enterprise technical evaluations; partner with Sales to improve win rate, pipeline coverage, and evaluation velocity.
- Develop technical strategy for Dust's most important enterprise opportunities and build understanding of why Dust wins and loses evaluations.
- Build the post-sales Solutions Engineering motion that helps customers adopt more advanced, valuable, and sticky use cases; partner with Customer Success on strategic workshops, complex integrations, and technically influenced expansion.
- Turn successful engagements into reusable playbooks, reference architectures, technical assets, and AI-native workflows; establish consistent methods for resource allocation, risk identification, and learning from outcomes.
- Raise the technical bar across Dust by turning recurring customer needs into clear input for Product and Engineering; strengthen technical positioning across enterprise AI, integrations, security, governance, and agent architecture.
Dust is an AI-native platform that empowers AI Operators at fast-moving companies to rewire how work gets done. The company has 70%+ weekly active users, serves customers like Datadog, 1Password, Cursor, Clay, Vanta, and Persona, and is backed by Sequoia. The team comes from Stripe, OpenAI, and Stanford.
The company prioritizes an in-person culture at offices in Paris, London, San Francisco, and New York, valuing the energy, fast decisions, and unexpected conversations that happen when talented people work closely together. Some flexibility is extended for working from home when it makes sense for the work.
REQUIREMENTS:
- You have built, scaled, or significantly transformed a Solutions Engineering, Solutions Architecture, Customer Engineering, Technical Account Management, or comparable customer-facing technical function.
- You have recruited, retained, and developed exceptional customer-facing technical talent across multiple regions, segments, or customer motions.
- You have owned measurable GTM outcomes and personally helped win complex enterprise opportunities.
- You understand both pre-sales technical execution and how post-sales Solutions Engineering can deepen adoption, value, and expansion.
- You combine strong technical credibility across enterprise architecture, integrations, security, governance, and AI systems with clear business judgment.
- You can remain close to strategic customers while building an organization that does not depend on you for every decision.
- You introduce the structure needed to scale without creating unnecessary process.
- You are a hands-on, low-ego leader who communicates clearly across Sales, Customer Success, AI Deployment, Product, and Engineering.
- You can advise customers on where AI agents create meaningful value; reason about model selection, prompting, context management, tool use, retrieval, evaluations, reliability, latency, and cost.
- You can explain the capabilities and limitations of modern AI systems clearly to technical teams, business leaders, and executive stakeholders.
- You can identify integration, security, data governance, and scalability risks; reason about APIs, authentication, data connectors, enterprise systems, and cloud architectures.
- You can design evaluation approaches that connect AI-system performance to customer and business outcomes and coach Solutions Engineers through difficult AI, architecture, and customer decisions.
