Manufacturing Data & Process AI Integration System Engineer (Additive Manufacturing)

• Monitoring 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

Benefits

• 100% coverage of platinum medical, dental, vision, and life insurance plans for employees
  401k
• Relocation stipend if you’re moving from outside of LA
• Flexible vacation policy- Proficiency in Python and relevant ML frameworks (scikit-learn, TensorFlow, PyTorch, or equivalent), and SQL fluency for working with manufacturing data at scale
• Ability to work on site full time in Torrance, California, with travel up to 15% [CONFIRM]
• 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
• Experience building analytical datasets from structured and time-series manufacturing data, including handling of gaps, resampling, and data quality problems
• Bachelor’s degree in Manufacturing Engineering, Computer Science, Data Science, Materials Science, or related field
• Strong analytical skills, with the ability to connect model outputs to physical process understanding and actionable engineering decisions
• Must be a U.S. person for ITAR purposes — a U.S. citizen, lawful permanent resident, protected individual as defined by 8 U.S.C. 1324b(a)(3), or otherwise eligible to obtain the required authorizations from the U.S. Department of State
• Familiarity with structured problem-solving methodologies (8D, 5 Whys, fishbone) and statistical process control
• Experience applying AI/ML to metal additive manufacturing — build quality prediction, anomaly detection, melt pool monitoring, or process parameter optimization
• Background with in-situ process monitoring data: layer imaging, thermal sensing, acoustic emissions, or scanner and galvanometer telemetry
• Experience supporting qualification data packages for aerospace, defense, or regulated manufacturing environments
• Familiarity with AMS7032, NIAR/NCAMP, or US Navy AM qualification requirements
• Experience with MLOps practices — model versioning, monitoring, retraining pipelines, and production deployment
• Experience with closed-loop or feedback control of a manufacturing process using model output