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Dust is an AI operating system that empowers enterprises to rewire how work gets done through collaborative human-agent interfaces. Backed by Sequoia and founded by alumni from Stripe, OpenAI, and Stanford, the company is growing rapidly with customers including Datadog, 1Password, and Cursor.
As Solutions Engineering Lead, you will build and lead the Solutions Engineering function from the ground up, connecting technical teams with customers, partners, and go-to-market organizations. You will own three functions: pre-sales technical evaluations, post-sales customer implementations, and partner engagements. Your responsibilities include defining team structure, career paths, operating standards, and success metrics; recruiting and onboarding 4–6 Solutions Engineers in your first year; and building repeatable playbooks for technical discovery, pilots, onboarding, implementation, and expansion.
You will remain hands-on with complex enterprise opportunities, maintaining deep expertise in Dust's API, data connectors, agent orchestration, prompt engineering, and integration architecture. You'll work directly with customer and partner engineering teams to validate integrations, build technical content including reference architectures and security documentation, and help customers understand how Dust agents differ from workflow automation and copilot products.
You will partner with Sales, Customer Success, Product, Engineering, and Partnerships to shape technical strategy, turning customer feedback into product roadmap input. You'll coach the team to use Dust daily and model AI-native ways of working, while establishing Dust's technical positioning across enterprise security, integrations, and agent architecture.
Requirements: At least three years leading technical teams across pre-sales, post-sales, solutions consulting, or related functions. Experience managing multiple Solutions Engineering functions. Strategic thinking combined with hands-on, low-ego leadership. Deep technical expertise in APIs, integrations, data connectors, and enterprise architecture. Ability to write production code in Python, TypeScript, Bash, or similar languages. Understanding of enterprise integration patterns, security, and compliance (REST APIs, webhooks, OAuth, SSO, SOC 2, GDPR). Ability to explain differences between agentic systems, workflow automation, and copilots to diverse audiences.
Ideal candidates have built or led Solutions Engineering organizations through early-stage growth, hired and developed teams across regions, worked with AI/LLM platforms, used AI-assisted development tools like Cursor or Lovable, and built technical partner programs.
