Manufacturing Data & Process AI Integration System Engineer, Additive Manufacturing

• 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.
• 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.
• 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.
• 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 (scikit-learn, TensorFlow, PyTorch, or equivalent), and SQL fluency for working with manufacturing data at scale.
• Experience building analytical datasets from structured and time-series manufacturing data, including handling of gaps, resampling, and data quality problems.
• Familiarity with structured problem-solving methodologies (8D, 5 Whys, fishbone) and statistical process control.
• Strong analytical skills, with the ability to connect model outputs to physical process understanding and actionable engineering decisions.
• Ability to work on site full time in Torrance, California, with travel up to 15% [CONFIRM].
• What Will Set You Apart
• 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.

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