Manufacturing Data & Process AI Integration System Engineer, Additive Manufacturing

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.

What You'll DoMonitoring and Analytics Layer:
Own the monitoring and analytics layer for the AM fleet — what is computed from raw machine and build data, what is surfaced, and what triggers an alert, at the fidelity traceability and modeling require.

Analytical Data Layer:
Own the curated datasets, feature definitions, labeling, and dataset versioning, built on the canonical machine data model and pipelines owned by the Machine Controls & Data Integration Engineer.

AI/ML Model Development:
Design, develop, and deploy models trained on Hadrian manufacturing data to predict build quality, detect process anomalies, and identify parameter optimization opportunities.

Model

Infrastructure: Build and maintain the feature engineering and model infrastructure — data quality checks, labeling workflows, model versioning, and model performance tracking in production.

Dashboards and Alerting:
Develop process monitoring dashboards and AI-driven alerting that give engineering and operations real-time visibility into machine and build health.

Closing the Loop:
Integrate model outputs back into OPUS and the manufacturing workflow so predictions drive action, and work toward closed-loop parameter adjustment.

Physical Validation with M&P:
Collaborate with Materials and Process and Application Engineering to validate model outputs against physical process knowledge before they influence production decisions.

Statistical Process Control:
Apply SPC to AM process data, and establish the control limits and drift detection that flag a machine leaving its qualified operating envelope.

Qualification Analysis Support:
Supply capability, repeatability, and process analysis in support of qualification — machine capability data to the System Qualification Engineer for installation and operational qualification, and performance qualification analysis support to Materials and Process and Application Engineering for customer data packages.

Data-Driven Problem Solving:
Lead structured problem-solving on process escapes and build anomalies using 8D, 5 Whys, and fishbone analysis, driving corrective and preventive action to verified closure.

What we're Looking For Bachelor's degree in Manufacturing Engineering, Computer Science, Data Science, Materials Science, or related field.
4+ years in manufacturing data systems, process engineering, or data-driven manufacturing in a production environment.

Hands-on experience developing and deploying AI/ML models in an engineering or manufacturing context, including model training, validation, and production deployment.

Proficiency in Python and relevant ML frameworks…