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San Francisco, California, United States
Onsite
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 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.
We're looking for someone who wants to lead from the work: technically strong, obsessed with building better systems, and excited to shape both the team and the function as we grow.
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.
Function-building experience: You've defined or significantly shaped a support engineering function before: built the operating model, set the standards, hired into the team, or rebuilt a broken process from scratch.
High technical aptitude: comfortable reading code, analyzing logs, navigating codebases, and troubleshooting distributed systems. You're not stopped by a stack trace.
AI fluency at builder level: track record of building custom agents, automations, or workflows using AI tools (Dust, Cursor, Claude Code, n8n, etc.); not just prompting.
Exceptional prioritization: You know what to solve immediately, what to automate, what to escalate, and, critically, what to delegate and to whom. You make these calls for yourself and for the team, fast and without second-guessing.
People leadership: You've coached and developed engineers and know how to set expectations, give direct feedback, and raise the performance of the people around you.
Bidirectional communication: can translate technical concepts to non-technical audiences and speak technical language fluently with engineers.
Build/Run mindset: You believe the best Support Engineering teams don't just resolve issues. They build systems that prevent those issues from recurring.
Builder mentality: You instinctively automate repetitive work rather than accepting inefficient systems, and you hold your team to the same standard.
Resilience-empathy paradox: You can absorb customer frustration without taking it personally while still deeply caring about the user experience.
Leads by example: You're not above the queue. When something is hard, you take it.
Exceptional prioritization: You know what to solve immediately, what to automate, and what to escalate.
Thrives in ambiguity: You enjoy building systems from scratch in environments where many processes are still undefined.
Low ego, team player: You share knowledge freely and care about the success of the broader team.
High ownership: You define what needs to be done as often as you execute it. You don't wait for someone to tell you the function has a gap.
Systems-first thinking: You focus on eliminating categories of work, not simply resolving tickets faster. You hold yourself and the team accountable to that distinction.
Pattern recognition to repeatability: You turn recurring issues into scalable systems, automations, workflows, or documentation that eliminate future occurrences.
Deep investigation instincts: You investigate as far as possible before escalating, providing engineering teams with thoughtful, high-context information when collaboration is needed, and you coach the team to do the same.
Even if you don’t meet every requirement above, we still encourage you to apply. We care deeply about curiosity, potential, and determination, and we know exceptional people don’t always fit neatly into a checklist.
Competitive compensation based on level and experience
$240,000 - $350,000
Significant equity package at a Sequoia-backed startup
Health benefits for you and your dependents
New MacBook Pro or Linux machine, monitor, keyboard, etc.
Opportunity to travel between our Paris and San Francisco offices
Regular team events and off-sites
We're prioritizing building our team with an in-person culture at our offices in Paris, London, San Francisco, and New York because we value the magic that happens when talented people work closely together.
We have an office-first culture. Some of the best things about building at Dust are the energy, the fast decisions, and the unexpected conversations that unlock a hard problem, which happen because we are in the same room. Being together is not a formality, it is how we do our best work, and it is something we actively protect.
That said, we hire people with strong judgement and we extend that trust to how they manage their time. When working from home makes more sense for what you need to get done that day, we trust you to make that call.
The models are powerful enough. What's missing is the product layer where AI meets how companies actually work. That's what we're building: the infrastructure that lets any team turn scattered knowledge and tools into coordinated execution with agents they build, own, and run themselves.
We use Dust ourselves every day. We get to shape how humans and agents collaborate while solving our own problems with the product we ship. That loop is rare, and it's why we move fast.
If you're excited about defining a new category and want to join a determined team of optimists who focus on users, ship fast, and don't take themselves too seriously, we'd love to talk.
Even if you don't check every box in our requirements, we encourage you to apply. We value diverse perspectives and backgrounds, and we're more interested in your potential and passion than a perfect match to our checklist.
Learn how we think and work.
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
