Machine Learning Engineer - Vision

Hadrian - Manufacturing the Future

Hadrian is building autonomous factories that help aerospace and defense companies manufacture rockets, satellites, jets, and ships up to 10x faster and up to 2x cheaper. By combining advanced software, robotics, and full-stack manufacturing, we are reinventing how America produces its most critical parts.

We’re accelerating our mission with the launch of Factory 3 in Mesa, Arizona, a 290,000-square-foot facility creating 350 new jobs. We are expanding rapidly to support thousands of future hires, launching Hadrian Maritime to expand into naval production, and introducing a Factory-as-a-Service model that delivers complete systems instead of individual parts.

Hadrian is backed by leading investors including T. Rowe Price, Lux Capital, Founders Fund, and Andreessen Horowitz, our fast-growing team is united around reindustrializing American manufacturing for the 21st century and beyond.

Copilot is our system for automating Design for Manufacturing (DFM) analysis and generating manufacturing processes. We work directly with some of the best operators in the world to identify high-impact opportunities to automate and augment with software.

Our team owns problems end-to-end: we design the software, define the manufacturing processes, and ensure they can be executed reliably in our factories. The work spans computational geometry, CAD/CAM integrations, high-performance systems, and full-stack web tooling. We execute whatever is required to deliver a working solution and best serve our users.

The DFM team within Copilot is building the manufacturing data intelligence layer that serves as the tip of the spear for our automation stack. This platform ingests, interprets, and reasons over the full spectrum of manufacturing data (mechanical drawings, quality documentation, CAD data) and transforms it into structured, actionable information for the factory.

As a Senior Machine Learning Engineer, you will own the ML lifecycle for the detection and segmentation models at the core of our manufacturing intelligence pipeline.

• Research, develop, and deploy cutting‑edge object detection and segmentation models for layout analysis, document understanding, and semantic part understanding
• Work alongside the core engineering team to build and maintain annotation tooling, implement active learning loops, and engineer synthetic data augmentation strategies
• Develop evaluation frameworks to capture metrics that extend beyond mAP/IoU, precisely quantifying system behavior and user impact
• Collaborate with the other members of the machine learning team to set the technical and product roadmaps for the AI platform
• Burn down the long tail, as every percentage point of accuracy maps to man‑years of time savings at our scale

• 5-8 years of professional experience building and shipping computer vision models, with an emphasis on detection and/or segmentation
• Deep expertise in modern architectures using transformer backbones (ViT, Swin, etc…)
• Strong Python and PyTorch fluency:
  You've written custom training loops, loss functions, and data loaders from scratch when needed
• Production deployment ownership:
  You've shipped models to production and have been responsible for endpoint and model health
• MS or PhD in Computer Science, Electrical Engineering, or related field preferred; equivalent industry experience valued equally

• You have a passion for manufacturing and believe that the industry needs better software
• Previously worked in aerospace, defense, or manufacturing, and have experience working with manufacturing data
• Published research, achieved SOA results on relevant benchmarks, or contribute to open‑source frameworks
• Prior experience working in a high‑ownership startup environment

For this role, the target salary range is $160,000 - $250,000 (actual range may vary based on experience).

This is the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee's pay position…