Manufacturing Software Research Engineer

Overview

The Manufacturing Software Research Engineer plays a key role in designing, developing, and integrating software systems for advanced manufacturing research, with a strong focus on machine learning, data science, and sensor integration. The engineer will work on internal research and cooperative development projects, supporting both CCAM teams and member organizations in expanding digital manufacturing capabilities. The role combines hands-on software development with applied analytics to derive actionable insights from complex manufacturing datasets.

This is a collaborative, on-site role working in a dynamic R&D setting. The Research Engineer will work closely with other Researchers and Industry Partners and must be comfortable engaging in cross-functional projects that blend software, hardware, and analytical thinking.

Software Development & Integration (80%)

• Develop software tools in Python, C++, or C# to support cooperative and member-driven research projects.
• Integrate systems using network protocols (TCP/IP) and middleware such as Zenoh, MQTT, DDS, OPC-UA.
• Create user interfaces for interacting with sensor systems, ML models, and 3D data.
• Utilize Docker for containerization and deployment of applications.
• Support cooperative processing projects by visualizing real-time networked data and performing 3D coordinate transformations.

Data Science & Analysis (10%)

• Conduct in-depth analysis of sensor and process data from additive manufacturing systems such as Laser Powder Bed Fusion (LPBF) and Directed Energy Deposition (DED).
• Use Python data libraries (Num Py, Pandas, OpenCV, Matplotlib) to identify features and anomalies in time-series and spatial datasets.
• Perform data parsing from custom formats and interact with databases using custom queries.
• Develop high-impact data visualizations to share findings with internal teams and external partners.
• Apply critical thinking to test hypotheses about manufacturing processes and part performance.

Machine Learning & Model Development (5%)

• Design and train models for segmentation, classification, and regression using Scikit-learn, Tensor Flow, or PyTorch.
• Apply machine learning to both numeric and image-based datasets.
• Conduct model tuning and evaluation, including hyperparameter optimization and data augmentation.
• Analyze and interpret results from experiments and integrate ML into real-time systems where applicable.

Software Evaluation & Technical Consultation (5%)

• Collaborate with CCAM partners to understand software needs in advanced manufacturing.
• Research, evaluate, and test third-party process intelligence tools for suitability.
• Develop and execute evaluation plans and present findings to internal stakeholders and member companies.

CCAM is the center of an ecosystem of collaboration with industry, government, and academia that is focused on helping manufacturers solve real world problems. CCAM works with its partners to develop and deploy new technologies that advance manufacturing and deliver business value. For many companies, joining CCAM can be the difference between being in front of new technology or behind the curve.

Our research focuses on building intelligence in manufacturing processes and systems by bringing experienced, multi-disciplinary teams together with advanced digital systems and manufacturing process capabilities as well as new technology to solve industrial challenges. Whether through new process development, process improvement, new technology development and insertion, system integration and automation, or advanced analytics and AI development, our teams deliver real, usable results.

Active research threads include flexible and distributed manufacturing, adaptive automation, cooperative processing, advanced coatings systems, and intelligent repair and replacement through new technologies.

As an applied research center, CCAM has a very strong, multi-disciplinary resident staff that includes research scientists and engineers, skilled industrial technicians, university faculty, and engineering interns and graduate students. These teams work collaboratively with one another as well as with partner…