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

• 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, Tensor Flow, 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 AMS
7032, 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