AI Support Engineer

About Dust Work is being rewritten, and the people holding the pen are the ones who actually run it. 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. About the Role 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. We’re hiring at multiple levels (L2-L4) depending on your experience and technical depth. Whether you’re early in your career with strong technical aptitude and hunger to learn, or you’re an experienced support engineer ready to build systems at scale, we want to hear from you. What you’ll do Build the AI systems that do the work 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. 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. Run support when the systems fall short 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. Bridge customer pain to product improvements Translate recurring customer issues into product signal that engineering and product teams can act on. Build strong working relationships with engineers to ensure efficient, high-context escalations. Own the feedback loop: when agents fail due to missing or incorrect information, close the gap across engineering, product, and documentation. Requirements Every candidate and employee’s success is measured against the same 3 dimensions: Aptitude, Attitude and Agency. Aptitude High technical aptitude: comfortable with APIs, debugging workflows, and lightweight scripting for internal tooling. 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. Technical investigation depth: comfortable reading code, analyzing logs, navigating codebases, and troubleshooting distributed systems. You’re not stopped by a stack trace. Bidirectional communication: can translate technical concepts to non‑technical audiences and speak technical language fluently with engineers. Interest in or experience with a Build/Run split: Whether you’ve already operated this way (for more senior levels) or you’re excited to learn this approach (for earlier career levels), you see support as more than ticket resolution. Attitude Builder mentality: You instinctively automate repetitive work rather than accepting inefficient systems. Resilience‑empathy paradox: You can absorb customer frustration without taking it personally while still deeply caring about the user experience. Proactive, not reactive: You anticipate problems and think beyond what customers explicitly ask for. Exceptional prioritization: You know what to solve immediately, what to automate, and what to accelerate. Sets boundaries while building trust: You communicate tradeoffs clearly and push back thoughtfully when needed. 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. Agency High ownership: You operate autonomously and drive outcomes forward without waiting for direction. Deep investigation instincts: You investigate as far as possible before escalating, providing engineering teams with thoughtful, high‑context information when collaboration is needed. Pattern recognition to repeatability: You turn recurring issues into scalable systems, automations, workflows, or documentation that eliminate future occurrences. Workflow optimization mindset: You continuously improve your own workflows and operating systems, treating leverage and efficiency as core parts of the job. Systems‑first thinking: You focus on eliminating categories of work, not simply resolving tickets faster. Bias toward building: You identify gaps proactively, build solutions without needing explicit permission, and continuously improve the support function around you. 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. Benefits & Compensation Competitive compensation: $100,000 to $170,000 a year 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 Location We’re prioritizing building our team with an in‑person culture at our offices in Paris and San Francisco, because we value the magic that happens when talented people work closely together. Why Dust 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. 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 #J-18808-Ljbffr