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.
TLDR;As a Solutions Engineering Lead at Dust, you will build and lead the function that connects our technical teams with customers, partners, and the broader go-to-market organization. You will shape how we support technical evaluations, customer implementations, and partner engagements, while remaining hands-on with our most complex opportunities. You will work closely with Sales, Customer Success, Product, Engineering, and Partnerships to build repeatable playbooks, develop the Solutions Engineering team, and show how Dust’s AI operating system can transform the way companies work.
Our culture
Product-First: Unlike others focused on building foundation models, we're laser-focused on creating delightful product experiences with existing LLMs.
Small, High-Impact Team: Join a team of alumni from Stripe, Square, OpenAI, and other top tech companies. We're intentionally keeping our team small and mighty – every individual has massive scope and impact.
Transparency & Collaboration: Our repository is open source, and we leverage serendipity between team members. We believe the best ideas emerge when the team shares information and insights openly.
Proactive Problem-Solving: If you see something broken, fix it—without waiting for permission. We don't wait for solutions - we create them.
Ship to Learn: We move fast and learn from real user feedback.
Intellectual Humility: We value strong convictions balanced with open-mindedness. Team members confidently advocate for their ideas while remaining receptive to new perspectives and evidence that might change their minds. If new data emerges, we adapt quickly.
Build and lead the Solutions Engineering organization
Lead the Solutions Engineering teams across three functions: pre-sales, post-sales, and partnerships.
Define the team structure, roles, career paths, operating standards, and success metrics for each function.
Recruit, hire, and onboard 4–6 Solutions Engineers across the three functions during your first year.
Build repeatable playbooks for technical discovery, pilots, onboarding, implementation, enablement, expansion, and partner co-selling.
Establish shared standards for technical capability, Dust platform expertise, demo craft, and customer communication.
Coach and develop the team through regular feedback, performance reviews, technical enablement, and leadership development.
Set and track performance metrics, including POC conversion, time to value, and partner-sourced pipeline.
Stay hands-on with technical work
Maintain deep expertise in Dust’s API, data connectors, agent orchestration, prompt engineering, and integration architecture.
Work directly with customer and partner engineering teams to validate integrations using APIs, Postman, and custom scripts.
Build reusable technical content, including reference architectures, security documentation, integration guides, and enablement materials.
Help customers and partners understand how Dust agents differ from deterministic workflow automation and copilot products.
Continue contributing to the most important and technically complex enterprise engagements.
Raise the technical bar across Dust
Partner with Sales, Customer Success, Product, Engineering, and Partnerships to shape technical strategy.
Turn customer and partner feedback into clear product and roadmap input.
Coach the team to use Dust every day and model AI-native ways of working.
Help establish Dust’s technical positioning across enterprise security, integrations, agent architecture, and workflow automation.
Requirements
You have at least three years of experience leading technical teams across pre-sales, post-sales, solutions consulting, technical services, or related functions.
You have experience managing or working across multiple Solutions Engineering functions, such as pre-sales, post-sales, implementation, or partnerships.
You combine strategic thinking with a hands-on, low-ego leadership style.
You have deep technical expertise across APIs, integrations, data connectors, and enterprise architecture.
You can write production code in Python, TypeScript, Bash, or a similar language, and have shipped real projects.
You understand enterprise integration patterns, security, and compliance requirements, including REST APIs, webhooks, OAuth, SSO, MCP, SOC 2, and GDPR.
You can explain the differences between agentic systems, workflow automation, and copilot products to technical and non-technical audiences.
Ideal, But Not Required:
You have built or led a Solutions Engineering organization through an early stage of growth.
You have experience hiring and developing Solutions Engineers across multiple regions or functions.
You have worked with AI, LLM, or agent platforms and understand concepts such as retrieval-augmented generation, prompt engineering, agent orchestration, and model evaluation.
You have used AI-assisted development tools such as Lovable, Cursor, or v0 to build demos, prototypes, or POC environments.
You have experience building technical partner programs, co-selling motions, or partner enablement.
You have influenced product strategy through structured customer and market feedback.
Benefits & Compensation
Competitive compensation based on experience
We offer substantial support for relocation, including a stipend up to €10k, finding an apartment in Paris, and supporting you with all residence and work permit-related procedures
Significant equity package in a Sequoia-backed startup
Health insurance for you and your dependents
New MacBook Pro or Linux machine, monitor, keyboard, etc.
Regular team events and offsites
We're prioritizing building our team with an in-person culture at our offices in Paris, 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