AI Support Engineering Lead (US)

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 among the Future 50.

At Dust, we're building for a new kind of AI Operator: people who rethink and rebuild how their teams work around AI. Not “how can AI help us do this faster?” but “if AI existed from day one, would we even do this the same way?”

As our AI Support Engineering Lead, you'll own how Support is built and scaled at Dust. This is a deeply hands-on role. You'll take on the hardest customer issues when our agents fall short, but your job isn't simply to resolve them. You'll look for the pattern behind each issue and turn what you learn into better agents, automation, tooling, documentation, and product improvements. Over time, you'll help grow the team and shape how Support Engineering operates as Dust scales.

You'll define, ship, and continuously iterate on the systems that make this possible: AI agents, automation workflows, classification systems, knowledge systems, and tooling built on top of Dust itself. You'll dogfood the product harder than almost anyone at the company and set the standard for how the rest of the Support Engineering team does the same.

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  Our product constitution, a story about our mission

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  Agents at work - Latent Space, podcast with our cofounder, Stanislas Polu, 2024

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  LLMs reasoning and agentic capabilities over time - dotAI, podcast with our cofounder, Stanislas Polu, 2024

• Set the technical direction for the support stack: what gets automated, in what order, and to what standard. You make the calls, not just the builds.

• Design, ship, and maintain AI agents and automation workflows that reduce manual support load: think ticket classification, acknowledgment automation, response drafting, incident detection, and proactive user outreach.

• Identify recurring categories of issues and engineer them out of existence through automation, documentation, prompt iteration, or product feedback.

• Build and maintain tooling (MCP integrations, Dust agents, internal scripts) that increase the team's capacity without increasing headcount.

• Define and own the "Support as a Product" backlog. You decide what goes on it, coach the team to execute it, and hold the bar on what shipped means.

• Hire, onboard, and develop the support engineers around you. You're not just resolving tickets and building systems, you're growing the people who will.

• Set the standard for how complex issues get handled at Dust. You take the hardest tickets yourself. The team knows what good looks like because they've watched you do it.

• Investigate complex issues across logs, code, and internal tooling to identify root causes and provide clear answers to customers.

• Handle escalated cases with precise, accessible communication for both technical and non-technical audiences.

• Systematically analyze agent-generated responses for inconsistencies and iterate on prompts, documentation, and tooling until human intervention is minimal.

• Represent support at the engineering and product level. You synthesize signal, prioritize it, and push it through with enough context that it actually moves things, not just relays it.

• Build strong working relationships with engineers and customer-facing teams to ensure efficient, high-context escalations.

• Own the feedback loop end-to-end: when agents fail due to missing or incorrect information, close the gap across engineering, product, and documentation.

Every candidate and employee's success is measured against the same 3 dimensions: Aptitude, Attitude and Agency.