Director, Engineering

Stratus

8d•Remote

About The Position

The growth is proven. Stratus has built a platform customers depend on and a market that is accelerating. The next chapter is scale and compounding value: more customers, deeper workflows, and an AI layer that gives MEP fabrication shops capabilities they could not build on their own. You will be among the first engineering leaders to put a genuine AI agent into a fabrication workflow that a real shop floor runs on. That has not been done at scale. This role is where it gets built. And you will build it while keeping the core product shipping to a rapidly growing customer base. You will need to hold both. You will report to the CTO, peer with Directors and Senior Directors across engineering, and partner day to day with the Head of Product and GTM leadership. This role is fully remote. We meet quarterly as a leadership team and make regular visits to customer shop floors. You will know what your software looks like when it is running in a real fabrication environment. Lead product focused teams delivering against the Stratus roadmap, bringing engineering and AI capability together to ship what customers depend on. We are early in this build and you will shape how it goes. MEP industry experience is a strong plus. Success in This Role Looks Like Customers notice the difference: core platform improvements and AI capabilities that address real workflow problems, solutions that fabrication shops come back for and build their operations around. AI shipped to production that sticks. Not a proof of concept. Something a shop floor runs on. Teams that raise their delivery bar consistently and a team stronger than when you found it. A seat earned in the architecture conversations that shape the platform because you brought engineering judgment and customer insight in equal measure.

Requirements

AI forward and pragmatic engineering. Has led a team shipping AI enabled product features that customers depend on in production. The bar is not a side project. It is something a customer paid for that would not exist without AI. Knows what it takes to keep AI reliable in production, makes the right build versus buy call, and focuses on shipping AI customers trust rather than infrastructure for its own sake.
Technical depth. Cloud native SaaS architecture with Azure experience preferred. Comfortable in C# or .NET. MongoDB or document database experience is a plus.
Experience. 10 or more years in software engineering including direct experience building and shipping AI enabled products, with 5 or more in engineering leadership at a SaaS product company. Has shipped in an environment where the customer base was growing fast enough that a bad release had real consequences. Has taken on a team, nurtured it to higher impact and done it without losing what was already working. Has hired at least one person who became exceptional and can explain exactly why.
Customer and product orientation. Can sit in a customer conversation, find the real problem beneath the stated request, and carry that signal back to the teams. Thinks first about what outcome the customer needs, then about how to deliver it. Measures success in value shipped, not tasks completed.
Leadership experience, including strong communication skills and ability to work with a distributed team and cross functional teams.
Domain (strong plus). MEP, fabrication, or construction technology experience lets you challenge product and architecture decisions with domain intuition and build customer credibility faster.

Nice To Haves

Experience with Autodesk or similar
Exposure to manufacturing, supply chain, or field operations software.
Hands on experience with LangGraph, MCP, or agentic orchestration frameworks.

Responsibilities

Engineering delivery. Own what ships. Lead PODs that build solutions customers notice and rely on. Favor working software and live demos over status decks. Set the cadence, clear the path, hold the bar.
Customer empathy and connection. Your customers work on a fabrication shop floor. Getting close to them means product conversations, design reviews grounded in real workflows, and staying present when a production issue reaches a customer. Build genuine understanding of what they are trying to accomplish and let that shape how your teams think and build.
Product solutioning. Work alongside Product and users to translate real workflow problems into solutions that stick. The solutions you build will be used on a shop floor by someone who needs them to work every time. Bring engineering judgment to what gets built: knowing when a targeted feature solves the problem, when a deeper architectural change is what is actually needed, and when the right answer is to simplify rather than add.
Technical architecture partnership. Work with the CTO and engineering leadership on platform decisions across our Azure and MongoDB Atlas stack and the move toward agentic surfaces. Full partner in the decisions and fully accountable for how your teams deliver them.
AI era engineering culture. Every stage of the development cycle, from a design whiteboard to a production deploy, should have AI making the team faster and the output better. You build that culture and you go first.
Engineering standards. Leaves the codebase, the process, and the people in better shape than you found them: CI/CD, code review culture, observability, and incident learning treated as foundations, not afterthoughts.
Cross-functional execution. Partner with Product, Data, and Customer Success to ship what customers depend on. Bring what you learn in the field back into how your teams build.