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Paris, Île-de-France, France
Hybrid
Available
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.
Dust is hiring a Lead Solutions Engineer to build the technical customer-facing organization required for our next stage of growth.
You will lead our global Solutions Engineering function across pre-sales and post-sales. You will own how Dust earns the technical win during complex enterprise evaluations and how we deepen the value, adoption, and stickiness of Dust during and after deployment through advanced use cases and technical solutions.
This is a founding player-coach role. During your first year, you will remain directly involved in our most important customer engagements while significantly scaling the team, and building the repeatable motions that allow the organization to operate without depending on you for every decision.
You will have authority over the organization’s structure, hiring, performance, career paths, technical standards, coverage, resource allocation, and operating model. As a member of Dust’s GTM leadership group, you will work closely with Sales, Customer Success, AI Deployment, Product, and Engineering to connect customer ambition, technical execution, and business value.
The opportunity is larger than building a traditional SE team. You will define how an AI-native Solutions Engineering organization helps customers move from understanding Dust’s potential to making it a critical part of how their companies operate.
Define the global vision, strategy, and operating model for Solutions Engineering at Dust.
Lead the organization across pre-sales and post-sales Solutions Engineering.
Design the team structure, leadership model, roles, career paths, coverage model, operating standards, and performance systems.
Significantly scale the team across Paris, New York, San Francisco and London.
Hire, develop, and manage both Solutions Engineers and future SE leaders.
Establish a high talent bar and build a team that combines technical depth, business judgment, executive presence, and customer empathy.
Create clear decision rights and operating rhythms across regions and functions.
Allocate Solutions Engineering resources based on opportunity complexity, customer impact, strategic importance, and likelihood of success.
Build the systems and leadership capacity required for the function to scale without relying on linear headcount growth.
Define how Dust qualifies, scopes, and executes complex enterprise technical evaluations.
Partner with Sales leadership to improve technical win rate, evaluation conversion, qualified pipeline coverage, and evaluation velocity.
Develop the technical strategy for Dust’s most important enterprise opportunities.
Build a clear understanding of why Dust wins and loses technical evaluations, then turn those insights into improvements across the organization.
Ensure successful evaluations transition into deployment with clear use cases, architecture, dependencies, ownership, and success criteria.
Raise performance across discovery, demonstrations, solution design, pilots, architecture reviews, security validation, and technical handoffs.
Build the post-sales Solutions Engineering motion that helps customers adopt more advanced, valuable, and sticky use cases.
Partner with Customer Success on strategic workshops, complex integrations, new architectures, and technically influenced expansion.
Establish a clear interface with AI Deployment, which owns initial implementation, enablement, production use, and first measurable value.
Turn successful engagements into reusable playbooks, reference architectures, technical assets, and AI-native workflows.
Establish consistent methods for allocating resources, identifying risk, inspecting opportunities, and learning from outcomes.
Reduce dependence on individual heroics while preserving the judgment required for complex enterprise situations.
Turn recurring customer needs into clear input for Product and Engineering.
Strengthen Dust’s technical positioning across enterprise AI, integrations, security, governance, and agent architecture.
You have built, scaled, or significantly transformed a Solutions Engineering, Solutions Architecture, Customer Engineering, Technical Account Management, or comparable customer-facing technical function.
You have recruited, retained, and developed exceptional customer-facing technical talent across multiple regions, segments, or customer motions.
You have owned measurable GTM outcomes and personally helped win complex enterprise opportunities.
You understand both pre-sales technical execution and how post-sales Solutions Engineering can deepen adoption, value, and expansion.
You combine strong technical credibility across enterprise architecture, integrations, security, governance, and AI systems with clear business judgment.
You can remain close to strategic customers while building an organization that does not depend on you for every decision.
You introduce the structure needed to scale without creating unnecessary process.
You are a hands-on, low-ego leader who communicates clearly across Sales, Customer Success, AI Deployment, Product, and Engineering.
Advise customers on where AI agents can create meaningful value
Reason about model selection, prompting, context management, tool use, retrieval, evaluations, reliability, latency, and cost.
Explain the capabilities and limitations of modern AI systems clearly to technical teams, business leaders, and executive stakeholders.
Identify integration, security, data governance, and scalability risks.
Reason about APIs, authentication, data connectors, enterprise systems, and cloud architectures.
Design evaluation approaches that connect AI-system performance to customer and business outcomes.
Coach Solutions Engineers through difficult AI, architecture, and customer decisions.
Recognize excellent technical work and hold the organization to a consistently high bar.
Competitive compensation
We offer substantial support for relocation including finding an apartment in Paris/NYC and supporting you with the visa procedure
Significant equity package in a Sequoia-backed startup
Health insurance for you and your dependents
New MacBook Pro or Linux machine, monitor, keyboard, etc.
Beautiful office in the heart of Paris or NYC
Opportunity to travel to the US/Paris multiple times a year
Regular team events and offsite
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
Your Next Co-Worker Isn’t Human, Stanislas Polu, VivaTech 2026. A conversation about AI agents taking on larger parts of work and how teams collaborate with them.
Dust: Gabriel Hubert on Enterprise AI, Gabriel Hubert, Nasdaq, published May 7, 2026. On Dust’s approach to enterprise AI and building the company in Europe.
