Senior Engineering Manager, Machine Learning

About the role

HADRIAN - MANUFACTURING THE FUTURE

Hadrian is building autonomous factories to reindustrialize America. By combining AI, advanced software, robotics, and full-stack manufacturing, we help aerospace and defense companies build rockets, satellites, aircraft, ships, and other mission-critical systems up to 10x faster and at significantly lower cost.

Following our $1.37B Series D at a $7.87B valuation, Hadrian is rapidly expanding our manufacturing footprint, launching new capabilities across welding, casting, forging, electronics, additive manufacturing, and more, while scaling our Factory-as-a-Service platform to transform how critical products are built.

Backed by leading investors including JPMorgan Chase, Valor Equity Partners, Andreessen Horowitz, Founders Fund, 137 Ventures, Lux Capital, T. Rowe Price, and Morgan Stanley, we’re building the future of American manufacturing—and looking for exceptional people to help make it happen.

If you’re ready to take on the most challenging and rewarding work of your career while helping create American manufacturing jobs for generations to come, you’re exactly who we’re looking for.

The Role

Technical Data Intelligence has the mission of making the world’s engineering data legible to automation. Ingesting high variance engineering design data in the form of engineering drawings and CAD models is crucial to assess part and assembly manufacturability, plan build processes, plan factories, and drive manufacturing process automation. This team includes both machine learning engineers and fullstack software engineers, and the product is tightly integrated with model development.

You will build and manage a team of machine learning engineers and software engineers focused on solving this problem. The solutions will be a mix of agent development, classical ML development, and systems for tracking, editing, and mutating this data. Your focus is on hiring, growing, and supporting a world class team. Ability and desire to go hands on will be necessary to help the team and stay in tune with what is possible as ML and software engineering rapidly change.

What You'll Do

- Own the people leadership of your engineers: hiring, onboarding, mentorship, career growth, and performance.

- Stay technically hands-on as a player-coach: participate in design reviews and contribute directly to the work when it helps the team move faster or clears a critical path.

- Set technical direction and where we invest headcount, in partnership with the engineers and technical leads who drive architecture and modeling decisions.

- Build and maintain the evaluation and annotation tooling the team depends on: gold datasets, accuracy metrics, and review loops where engineers correct model output and feed it back into the system.

- Work across ML and software engineering so that model development and the product ship together.

- Ensure engineers are engaged, growing, and set up to do their best work.