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Active2d ago

Global Head of AI Deployment & Partner Delivery (US)

Dust·📍 New York, United States

Employment

FULL TIME

Work mode

On-site

Experience

6+ years

Salary

$300K – $350K/yr

Role overview

Job description

About Dust Work is being rewritten, and the people holding the pen are the ones who actually run it. With enterprise-grade governance, flexible model choice, and a collaborative interface for humans and agents to work together, Dust empowers AI Operators at the world’s fastest-moving companies to rewire how work gets done. With 70%+ weekly active users, people stick with Dust as much as they do with Slack and Notion. We don't get piloted and shelved. We land once, and spread. We're at an exciting stage of our journey, and growing fast. We're serving great customers like Datadog, 1Password, Cursor, Clay, Vanta and Persona, and aim to x5 our growth by the end of 2026. Dust is backed by Sequoia with a determined team of optimists (coming from Stripe, OpenAI, and Stanford) who like to focus on users, ship fast, and don't take themselves too seriously while doing so. The Generalist named us a

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About this listing

  • Source: Dust's official careers site (via Ashby) · last confirmed on the source 30 Oct 2026
  • Posted 29 Oct 2026

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What they are looking for

Skills & requirements

Qualification

Co-create onboarding, training, certification, and train-the-trainer programs so partners can develop their own Dust experts; Requirements You have at least 6 years of experience leading and developing customer-facing deployment or delivery teams, ideally across multiple regions or customer segments and through periods of fast change; You understand how systems integrators, consultancies, and implementation partners staff, deliver, and grow a practice, and you have built enablement or certification programs that created lasting capability in teams you didn’t manage

Department · Customer Experience

Hiring company

Dust

Dust connects your company knowledge, tools, and teams so you can create, share, and run agents across real workflows. Use different models for different tasks, with people in control.

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