Support Engineer at Dust

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

31 May, 26

Salary

0.0

Posted On

02 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Debugging, Log Analysis, Code Navigation, Root Cause Analysis, Quality Assurance, Prompt Engineering, Documentation, Communication, Analytical Precision, Problem Solving, Prioritization, Multitasking, Ownership, Initiative, Adaptability, Tooling Development

Industry

Software Development

Description

About Dust We're creating a new AI operating system that has the potential to change the way companies operate. Our mission at Dust is to transform how work gets done by letting any team and employee shape the exact agents they need to accelerate their work. With 70%+ weekly active users, people stick with Dust as much as they do with Slack and Notion. We already are a core part of their jobs. We're at an exciting stage of our journey and growing fast. We're serving great customers like Cursor, Clay, Whatnot, and Persona, and aim to 5x our growth by the end of 2026. Dust is a Sequoia-backed company with an experienced and determined team of optimists (coming from companies like Stripe and OpenAI) that likes to focus on users, getting great things done by shipping fast, and doesn’t take itself too seriously while doing so. TLDR Dust is building the AI operating system that transforms how companies work, already used weekly by 70%+ of our customers' teams. We're looking for a Support Engineer to define what AI-native support looks like in practice. The role The Customer Success team at Dust partners with our customers to transform how they work with AI. We're looking for a Support Engineer to work alongside our support agents to deliver fast, precise, and delightful user experiences. Your responsibilities will center on three pillars: quality assurance of agent-generated responses until the vast majority require minimal human intervention, knowledge gap closure by ensuring missing information is systematically documented, and debugging issues through to a well-defined hypothesis. You will define what AI Agentic support looks like in practice. What you'll do Resolve our users issues: Investigate issues across historical patterns, logs, code, and our back-office tooling. Develop hypotheses, identify root causes, and provide clear answers. Quality assurance of conversations: Systematically analyze agent responses for inconsistencies, redundancies, and ambiguities. Continuously iterate on prompts, documentation, and tooling until human intervention is minimal. Knowledge gap identification and closure: When the agent fails due to missing or incorrect information in documentation, code, or internal resources, ensure the appropriate stakeholders document the missing knowledge. Maintain accountability for closing gaps through persistent, collaborative follow-up. Inbox coverage: Handle complex cases escalated by the agent. Produce responses that are simple, legible, obvious, short, and self-explanatory. Function development: Contribute to the tooling, automations, and prompt engineering that improve agent performance. The objective is a single, elegant system capable of handling the majority of support interactions. Requirements Excellent communication skills in English to effectively engage with a diverse range of users, from various backgrounds. Technical debugging capability: Proficiency in reading code, analyzing logs, and investigating technical issues. Full engineering expertise is not required, but comfort navigating a codebase is a must-have. Analytical precision: Demonstrated ability to identify inconsistencies, edge cases, and ambiguities that others overlook. A systematic approach to problem identification and resolution. Collaborative persistence: Ability to drive knowledge documentation and process improvements across teams. Effective at maintaining accountability while preserving collaborative relationships. Documentation skills: Capacity to produce documentation and responses that are concise, precise, and accessible to both technical and non-technical audiences. Curiosity, eagerness to learn, and adaptability with a flair for elegant solutions to first-of-a-kind problems. Robust prioritization and multitasking skills to manage multiple customers simultaneously while building organizational infrastructure as an early team member. High level of ownership and initiative with a "doer attitude". Ability to operate alongside demanding users in an emerging field, driving outcomes effectively. Benefits & Compensation 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 to the EU multiple times a year 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 We're not building yet another enterprise SaaS tool. We’re creating an AI OS that will fundamentally change how companies operate. We believe existing AI models are powerful enough to have a tremendous impact on the world (and will keep getting better). The key is building the infrastructure so they have the right context, and building the best interfaces for humans to interact with them. We have the unique opportunity to explore and shape the way humans interact with machines while building a product we use ourselves every day. If you're excited about crafting products that reinvent B2B software and want to join a team that combines the best of startup culture with the backing of top-tier investors, 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 Stanislas Polu (2024)

Responsibilities

The role centers on defining AI-native support by ensuring quality assurance of agent-generated responses, systematically closing knowledge gaps through documentation, and debugging issues to form clear hypotheses. Responsibilities include resolving user issues by investigating patterns and logs, handling escalated complex cases, and contributing to tooling and prompt engineering to automate support interactions.