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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manufacturing Data & Process AI Integration System Engineer - **Company:** Hadrian Inc. - **Location:** Torrance, CA, United States - **Experience:** Experienced - **Salary:** $135,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Customer Data Management, Data Validation, Data Integration, Python (Programming Language), Machine Learning, Tensorflow, Software Deployment, Statistical Process Control (SPC), SQL Databases, Technical Data Management Systems, Management of Software Versions, Data Processing, Feature Engineering, Pytorch, Model Validation, Scikit Learn, Information Technology, Data Analytics, Machine Learning Operations, OPUS (Software), Software Version Control - **Published:** September 27, 2026 - **Apply:** https://www.thejobnetwork.com/job/771cedd2-f39d-43fc-80ac-47de1ea3986b/manufacturing-data-process-ai-integration-system-engineer-additive-manufacturing ## About the Role * 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]. * 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. 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. ## Description * 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. ## Related Videos - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Industrial AI: Built for reality, operation, and people](https://www.wearedevelopers.com/videos/2074-industrial-ai-built-for-reality-operation-and-people) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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