Machine Learning Engineer, Hadrian

• 5-8 years of professional deep learning experience, with at least 2 years working on multimodal models leveraging image and text modalities
• 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
• To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here

Desirable

• 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 vision-language models that unify our perception and reasoning capabilities, building systems that understanding manufacturing drawings and design intent
• Research, develop, and deploy multimodal models that understand content from manufacturing documentation end-to-end, replacing traditional OCR-to-NLP cascades with unified architectures
• 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 that quantify system behavior and user impact, extending beyond traditional benchmark metrics
• 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