AI Support Engineer (US)

We call them AI operators: the employees inside companies who build, deploy, and run AI agents for their teams, without waiting for someone to hand them a tool. Dust is the platform they choose to rewire how their company works.

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, 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 coining the term AI Operator: someone who rethinks and rebuilds company processes 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?"

The AI Support Engineer applies this mindset to Support. You’ll split your time between running support (handling complex issues when agents fall short) and building the AI systems that reduce that workload over time. Support at Dust is not a cost center, it’s a product. Success is measured by eliminating categories of tickets, not just resolving them.

You will define, ship, and continuously iterate on the infrastructure that lets Dust deliver a world-class support experience at scale: AI agents, automation workflows, classification systems, knowledge systems, and tooling built on top of Dust itself. You will dogfood the product harder than almost anyone at the company.

• 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 entire categories of issues and engineer them out of existence: better documentation, tighter agent prompts, or upstream product feedback.

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

• Contribute to the evolving "Support as a Product" backlog. You will own tasks with shipped/doing/todo statuses like a product engineer, not a queue operator.

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

• Handle complex, escalated cases with precise and accessible communication

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

• When the agent fails due to missing or incorrect information, close the gap — own the follow-up across engineering, product, and documentation.

• Our product constitution, a story about our mission

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

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