Engineering Manager, Hadrian

• 2-5+ years of engineering management experience leading machine learning engineering teams in production, with strong technical depth and experience in machine learning, computer vision (detection/segmentation), multimodal image+text models, and/or data engineering before that
• Strong judgment on organizational design: how to structure a team, allocate headcount across projects, and balance depth vs. breadth as the stack grows
• Production deployment ownership: you've shipped models to production and been responsible for endpoint and model health
• A track record of running a healthy team: clear expectations, direct feedback, and a strong sense of ownership among your engineers
• Heavy interest in manufacturing: you either have past exposure to it or are excited to learn as much about it as possible from experts here and help others do the same. This is core to our culture
• Excellent communication and cross-functional partnership skills: you can align engineers, technical leads, and leadership

Desirable

• 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
• You will build and manage a team of engineers focused on solving this problem. The solutions will be mix of agent development, classical ML development, and systems for tracking, editing, and mutating this data. This is a people-first management role: your focus is on hiring, growing, and supporting a world class team, while staying close to the technical work to help guide direction and staffing decisions, jumping in when necessary to unblock the critical path or understand the problems and technology on a deeper level
• 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 annotation tooling, implement active learning loops, and engineer synthetic data augmentation strategies
• Ensure engineers are engaged, growing, and set up to do their best work