Hadrian

Manufacturing Data & Process AI Integration System Engineer, Additive Manufacturing

Hadrian • $110K — $130K *
Manufacturing & Automotive
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 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.
  • Hands-on experience developing and deploying AI/ML models in an engineering or manufacturing context.
  • Proficiency in Python and relevant ML frameworks (scikit-learn, TensorFlow, PyTorch, or equivalent) and SQL fluency.
  • Experience building analytical datasets from structured and time-series manufacturing data.
  • Familiarity with structured problem-solving methodologies (8D, 5 Whys, fishbone) and statistical process control.
  • Strong analytical skills connected to process understanding and engineering decisions.

Responsibilities

  • Own the monitoring and analytics layer for the AM fleet, determining data outputs and alert triggers.
  • Manage curated datasets, feature definitions, and versioning based on machine data models.
  • Design, develop, and deploy AI models for predicting build quality and detecting anomalies.
  • Build and maintain model infrastructure, ensuring data quality, labeling, and performance tracking.
  • Develop dashboards and AI alerting for real-time engineering and operations visibility.
  • Integrate model outputs into workflows for actionable predictions and closed-loop adjustments.
  • Collaborate with engineering teams to validate model outputs against physical processes.
  • Apply SPC to identify machine performance deviations in the manufacturing process.
  • Provide analysis support for machine capability and operational qualifications.

Benefits

  • Medical, dental, vision, and life insurance plans for employees.
  • 401k retirement plan.
  • Relocation support based on business needs.
  • Flexible vacation policy.
  • Equity options for employees.
Full Job Description
What You'll Do
  • 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.

What we're Looking For
  • 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.
Benefits for Full-time Employees
  • Medical, dental, vision, and life insurance plans for employees
    401k
  • Relocation support may be provided for certain situations, based on business need.
  • Flexible vacation policy
  • Equity

About Hadrian

Hadrianadri?ja?n?s]; 24 January 76 – 10 July 138) was Roman emperor from 117 to 138. He was born in Italica, a Roman municipium founded by Italic settlers in Hispania Baetica and he came from a branch of the gens Aelia that originated in the Picenean town of Hadria, the Aeli Hadriani. His father was of senatorial rank and was a first cousin of Emperor Trajan. Hadrian married Trajan's grand-niece Vibia Sabina early in his career before Trajan became emperor and possibly at the behest of Trajan's wife Pompeia Plotina. Plotina and Trajan's close friend and adviser Lucius Licinius Sura were well disposed towards Hadrian. When Trajan died, his widow claimed that he had nominated Hadrian as emperor immediately before his death. Rome's military and Senate approved Hadrian's succession, but four leading senators were unlawfully put to death soon after. They had opposed Hadrian or seemed to threaten his succession, and the Senate held him responsible for their deaths and never forgave him. He earned further disapproval among the elite by abandoning Trajan's expansionist policies and territorial gains in Mesopotamia, Assyria, Armenia, and parts of Dacia. Hadrian preferred to invest in the development of stable, defensible borders and the unification of the empire's disparate peoples. He is known for building Hadrian's Wall, which marked the northern limit of Britannia.
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