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

Hadrian; New Mexico; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Insurance coverage (medical, dental, 


401(k)

Relocation stipend

Flexible vacation policy

Job summary

Hadrian is seeking an experienced AI/ML Engineer to own and advance the analytics and modeling stack for additive manufacturing. You will design, deploy, and monitor models trained on Hadrian manufacturing data to predict build quality and detect anomalies, while building robust data pipelines and dashboards.

You will collaborate with Materials and Process and Application Engineering to validate model outputs against physical process knowledge and support qualification activities.

Qualifications

• Bachelor's degree in a relevant 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 engineering/manufacturing context.
• Experience building analytical datasets from structured and time-series manufacturing data.

Responsibilities

• Own the monitoring and analytics layer for the AM fleet, defining what is computed from raw data and what surfaces as alerts.
• Own curated datasets, feature definitions, labeling, and dataset versioning from canonical machine data models.
• Design, develop, and deploy AI/ML models to predict build quality, detect process anomalies, and optimize parameters.
• Build and maintain feature engineering and model infrastructure, including data quality checks and model versioning.
• Develop process monitoring dashboards and AI-driven alerting for real-time visibility into machine/build health.
• Integrate model outputs back into OPUS and manufacturing workflows to drive action.
• Collaborate with Materials and Process and Application Engineering to validate model outputs against physical knowledge.
• Apply Statistical Process Control to AM data and establish control limits and drift detection.
• Provide analysis support for qualification, including machine capability data for System Qualification Engineers.

Skills

Python

ML frameworks

SQL

AI/ML model deployment

Education

Bachelor's degree in Manufacturing Engineering / CS / Data Science / Materials Science or related field

Tools

scikit-learn

TensorFlow

PyTorch

Job 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

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 scaleAbility 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 environmentHands-on experience developing and deploying AI/ML models in an engineering or manufacturing context, including model training, validation, and production deploymentExperience building analytical datasets from structured and time-series manufacturing data, including handling of gaps, resampling, and data quality problemsBachelor's degree in Manufacturing Engineering, Computer Science, Data Science, Materials Science, or related fieldStrong analytical skills, with the ability to connect model outputs to physical process understanding and actionable engineering decisionsMust 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 StateFamiliarity with structured problem-solving methodologies (8D, 5 Whys, fishbone) and statistical process controlExperience applying AI/ML to metal additive manufacturing - build quality prediction, anomaly detection, melt pool monitoring, or process parameter optimizationBackground with in-situ process monitoring data: layer imaging, thermal sensing, acoustic emissions, or scanner and galvanometer telemetryExperience supporting qualification data packages for aerospace, defense, or regulated manufacturing environmentsFamiliarity with AMS7032, NIAR/NCAMP, or US Navy AM qualification requirementsExperience with MLOps practices - model versioning, monitoring, retraining pipelines, and production deploymentExperience with closed-loop or feedback control of a manufacturing process using model output