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Dust is hiring a Global Head of AI Deployment & Partner Delivery to scale high-impact AI deployments across enterprise customers and a growing partner network.
Dust is an enterprise AI platform backed by Sequoia that empowers teams to build, own, and run AI agents integrated with their internal data and workflows. The company serves customers like Datadog, 1Password, Cursor, and Vanta, with 70%+ weekly active user retention comparable to Slack and Notion.
In this player-coach leadership role, you will:
**Lead and Grow the Global Deployment Team**
Build and develop Dust's global AI Deployment team, setting direction, priorities, and standards. Hire and develop deployment leaders and practitioners as customer demand grows. Shape roles, career paths, and regional coverage. Coach teams through complex customer situations and establish operating rhythms, capacity planning, and reporting needed to scale.
**Define Dust's Deployment Methodology**
Create a unified deployment method from discovery through handoff that both Dust and partner teams follow to the same standard. Define which deployments Dust should lead versus partner-led delivery. Establish entry/exit criteria and go-live readiness checks. Track delivery health metrics: time to first value, go-live quality, and customer satisfaction. Run regular delivery reviews to spot at-risk engagements early. Convert lessons learned into reusable playbooks, templates, and reference architectures.
**Lead AI Transformation with Strategic Customers**
Personally lead complex, high-priority, and first-of-their-kind deployments from scoping through lasting adoption. Help customer leaders define what AI transformation means for their organization by mapping current workflows and identifying where agents create the most value. Align executives, IT, security, and business teams on goals and success measures. Design change programs including executive sponsorship, communication plans, role-based enablement, and adoption milestones. Develop AI operators and champions inside customer teams. Track post-launch adoption and hand off to Customer Success with clear next steps.
**Build a High-Quality Partner Delivery Network**
Work with Partnerships to assess partner delivery capability, technical depth, and customer readiness. Start with a carefully selected group and grow capacity as quality is proven. Co-create onboarding, training, certification, and train-the-trainer programs. Co-deliver early partner engagements, then help partners progress toward independent delivery. Ensure customers experience Dust and partners as one aligned team.
**AI and Technical Credibility**
Advise customers on where AI agents create meaningful value and explain limitations to technical and business leaders. Understand how agents work in practice: prompting, context, tool use, retrieval, evaluations, reliability, and cost. Challenge deployment assumptions about integrations, data connectors, authentication, permissions, security, and production readiness. Design evaluation approaches connecting AI performance to customer and business outcomes.
Dust values an office-first culture at its Paris, London, San Francisco, and New York offices, believing that in-person collaboration drives fast decisions and breakthrough problem-solving. The company extends trust to strong judgment and allows flexibility for remote work when it makes sense for the task at hand.
**Requirements**
- Minimum 6 years leading and developing customer-facing deployment or delivery teams, ideally across multiple regions or customer segments through periods of fast change
- Experience leading complex enterprise deployments or transformation programs with measurable customer outcomes
- Track record building or significantly improving a deployment methodology, professional services model, or delivery function that scaled without unnecessary process
- Experience leading organizational change alongside technology rollouts and building champion networks or adoption plans that changed how teams work after project completion
- Understanding of how systems integrators, consultancies, and implementation partners staff, deliver, and grow practices; experience building enablement or certification programs that created lasting capability in teams you didn't manage
- Proven ability to turn around at-risk engagements from scope, staffing, technical complexity, or stakeholder misalignment; judgment about when to standardize, adapt, or reset
- Credibility with customer executives, partner leaders, and technical practitioners; clear communication across all audiences
- Hands-on, low-ego leader with a builder's instinct; preference for solving problems over handing off recommendations
- Comfort traveling to customers and partners when it matters most
- Ability to advise customers on where AI agents create meaningful value and explain limitations to technical and business leaders
- Understanding of how agents work in practice: prompting, context, tool use, retrieval, evaluations, reliability, and cost
- Ability to challenge deployment assumptions about integrations, data connectors, authentication, permissions, security, and production readiness
- Experience designing evaluation approaches connecting AI performance to customer and business outcomes
Note: Machine-learning engineering background is not required. The company values evidence of building high-performing delivery systems over a perfect sequence of titles.
