Manufacturing Data & Process AI Integration System Engineer, Additive Manufacturing
About this role
The Manufacturing Data & Process AI Integration System Engineer will join Hadrian, a company focused on building autonomous factories to accelerate the reindustrialization of America. Hadrian leverages AI, robotics, and full-stack manufacturing software to help aerospace and defense firms produce mission-critical systems more quickly and affordably. This role is central to enhancing the performance and intelligence of their Additive Manufacturing (AM) fleet as the company expands its capabilities across various manufacturing processes.
This engineer will be responsible for creating the intelligence layer that monitors machine and build data, transforming raw information into actionable insights. Key responsibilities involve developing AI/ML models to predict build quality, detect anomalies, and suggest parameter optimizations. The engineer will bridge the gap between data science and physical process engineering by ensuring model outputs are validated against real-world manufacturing knowledge.
This position is ideal for a candidate who enjoys applying machine learning directly to complex physical systems, thrives on data curation, and possesses strong analytical skills. The successful candidate will contribute directly to the development of closed-loop systems, driving continuous improvement in manufacturing processes.
What you will do
- Own the monitoring and analytics layer for the AM fleet, determining what data is computed, surfaced, and alerts based on fidelity traceability and modeling requirements.
- Manage the curated datasets, feature definitions, labeling processes, and dataset versioning, ensuring they adhere to the canonical machine data model.
- Design, develop, and deploy machine learning models trained on Hadrian manufacturing data to predict build quality, identify process anomalies, and suggest parameter optimizations.
- Build and maintain the necessary model infrastructure, including data quality checks, labeling workflows, model versioning, and performance tracking in production.
- Develop process monitoring dashboards and AI-driven alerting systems to provide engineering and operations teams with real-time visibility into machine and build health.
- Integrate model outputs back into the OPUS workflow to enable predictions to drive actual parameter adjustments in the manufacturing process.
- Collaborate with Materials and Process and Application Engineering to validate model predictions against physical process knowledge before they impact production decisions.
- Apply Statistical Process Control (SPC) to AM process data to establish control limits and detect drift in machine operation.
What you need
- A Bachelor's degree in Manufacturing Engineering, Computer Science, Data Science, Materials Science, or a related field is required.
- Candidates must possess at least four years of experience in manufacturing data systems, process engineering, or data-driven manufacturing within a production environment.
- Hands-on experience is necessary in developing and deploying AI/ML models within an engineering or manufacturing context, covering training, validation, and production deployment.
- Proficiency in Python and relevant ML frameworks such as scikit-learn, TensorFlow, or PyTorch, alongside strong SQL fluency for handling large-scale manufacturing data.
- Experience building analytical datasets from structured and time-series manufacturing data, including addressing challenges like data gaps, resampling, and quality issues.
- Familiarity with structured problem-solving methodologies like 8D, 5 Whys, and fishbone analysis, as well as Statistical Process Control principles.
- Strong analytical capabilities are needed to connect model outputs with physical process understanding to generate actionable engineering decisions.
- The role requires the ability to work on-site full-time in Torrance, California, with travel up to 15%.
Nice to have
- Experience applying AI/ML to metal additive manufacturing
- Background with in-situ process monitoring data, such as layer imaging, thermal sensing, or acoustic emission imaging
Benefits
- Offers Equity
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
The job posting indicates that the role requires working on-site full time in Torrance, California, with travel up to 15%.
What is the compensation range for this position?
The salary offered for this role ranges from $135K to $220K, and the compensation also includes equity.
What specific AI/ML technologies are used?
The required tech stack includes proficiency in Python and ML frameworks like scikit-learn, TensorFlow, and PyTorch.
How is the team structured?
The role involves collaboration with the Machine Controls & Data Integration Engineer and Materials and Process and Application Engineering.
The application is on the official hadrian-automation careers page.
Apply on company site