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Hadrian

Manufactures precision parts in automated factories

Manufacturing Data & Process AI Integration System Engineer - Additive Manufacturing

Applied Machine LearningConfirmed live in the last 24 hours
Full-time$135k - $220k /yr

About the Job

Requirements

  • A bachelor's degree in Manufacturing Engineering, Computer Science, Data Science, Materials Science, or a related field is required.
  • At least 4 years of experience in manufacturing data systems, process engineering, or data-driven manufacturing in a production environment is required.
  • Hands-on experience developing and deploying AI/ML models in an engineering or manufacturing context, including model training, validation, and production deployment, is required.
  • Proficiency in Python and relevant machine-learning frameworks such as scikit-learn, TensorFlow, PyTorch, or equivalent, and fluency in SQL for working with manufacturing data at scale are required.
  • Experience building analytical datasets from structured and time-series manufacturing data, including handling gaps, resampling, and data-quality problems, is required.
  • Familiarity with structured problem-solving methodologies, including 8D, 5 Whys, and fishbone analysis, and statistical process control is required.
  • The candidate must be able to connect model outputs to physical process understanding and actionable engineering decisions.
  • The candidate must work on site full time in Torrance, California, and travel up to 15%.
  • The candidate 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 required authorizations from the U.S. Department of State.

Responsibilities

  • Own the monitoring and analytics layer for the additive manufacturing fleet, defining what is computed from raw machine and build data, what is surfaced, and what triggers an alert at the fidelity required for traceability and modeling.
  • Own 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.
  • Design, develop, and deploy models trained on manufacturing data to predict build quality, detect process anomalies, and identify parameter-optimization opportunities.
  • Build and maintain feature-engineering and model infrastructure, including data-quality checks, labeling workflows, model versioning, and production performance tracking.
  • Develop process-monitoring dashboards and AI-driven alerting that provide engineering and operations with real-time visibility into machine and build health.
  • Integrate model outputs into OPUS and the manufacturing workflow so predictions drive action, and work toward closed-loop parameter adjustment.
  • Collaborate with Materials and Process and Application Engineering to validate model outputs against physical process knowledge before they influence production decisions.
  • Apply statistical process control to additive manufacturing process data and establish control limits and drift detection that flag a machine leaving its qualified operating envelope.
  • Provide capability, repeatability, and process analysis in support of qualification, including machine-capability data for the System Qualification Engineer's installation and operational qualification and performance-qualification analysis for customer data packages prepared by Materials and Process and Application Engineering.
  • Lead structured problem-solving on process escapes and build anomalies using 8D, 5 Whys, and fishbone analysis, driving corrective and preventive actions to verified closure.

Desired Qualifications

  • Experience applying AI/ML to metal additive manufacturing, such as build-quality prediction, anomaly detection, melt-pool monitoring, or process-parameter optimization.
  • Background with in-situ process-monitoring data, including 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 U.S. Navy additive-manufacturing qualification requirements.
  • Experience with MLOps practices, including model versioning, monitoring, retraining pipelines, and production deployment.
  • Experience with closed-loop or feedback control of a manufacturing process using model output.

About the Company

Hadrian

Manufactures precision parts in automated factories

Hadrian manufactures precision components for aerospace and defense companies through automated factories. Customers buy parts from prototype through production, reserve dedicated manufacturing and inspection cells, or engage Hadrian to design and operate factories for their products. Its Opus platform interprets technical designs and coordinates manufacturing and inspection, combining software with factory equipment and trained operators. Production services run in Hadrian facilities or within customer facilities, depending on the engagement. Its Atlas service manages procurement from vetted suppliers, extending the offering to parts sourced outside its factories.

Company size
201-500
Company stage
Debt Financing
Total funding
$2.3B
Headquarters
Torrance, CA
Founded
2020

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