AI-Driven Additive Manufacturing Data & Process Engineer

Front Door Defense; Torrance, Northern (CA, KY); Hybrid; USD 135,000 - 220,000; Full time

Benefits offered by this job

Medical, dental, vision, and life

Relocation support

Flexible vacation

Equity

Job summary

Hadrian in Torrance, CA is seeking a Manufacturing Data & Process AI Integration System Engineer for additive manufacturing to build AI-driven quality prediction models and deploy them across the production floor.

The role combines analytics, data engineering, and model deployment to predict build quality, detect process anomalies, and optimize parameters while ensuring traceability and integration with OPUS and production systems.

Qualifications

• Bachelor's degree in Manufacturing Engineering or related field.
• 4+ years in manufacturing data systems or data-driven manufacturing.
• Hands-on experience developing and deploying AI/ML models in manufacturing.
• Proficiency in Python and ML frameworks, and SQL fluency.
• Experience with data pipelines and model versioning.

Responsibilities

• Monitor and analytics layer for AM fleet data and alerts.
• Own curated datasets, feature definitions, labeling, and versioning.
• Design, train, and deploy AI/ML models on Hadrian manufacturing data.
• Build feature engineering and model infrastructure with data quality checks.
• Develop dashboards and AI-driven alerts for real-time visibility.
• Integrate model outputs into OPUS and production workflows.
• Collaborate with Materials, Process, and Applications teams for validation.
• Apply SPC and control limits to AM process data.
• Support qualification analyses with machine capability data.
• Lead data-driven problem solving using 8D, 5-Whys, Fishbone.

Skills

Python

ML frameworks

SQL

Manufacturing data

Analytical problem solving

Education

Bachelor's degree in Manufacturing Engineering

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Hadrian in Torrance, CA is seeking a Manufacturing Data & Process AI Integration System Engineer for additive manufacturing to build AI-driven quality prediction models and deploy them across the production floor.

The role combines analytics, data engineering, and model deployment to predict build quality, detect process anomalies, and optimize parameters while ensuring traceability and integration with OPUS and production systems.