Sr. Computer Vision Engineer

About Vulcan Forms

Founded in 2015 to develop the world’s first industrially scalable laser metal additive manufacturing solution, Vulcan Forms reshapes how the world manufactures critical products. We build and operate advanced digital infrastructure that enables innovation, accelerates growth, and supports sustainability across aerospace, defense, medical, and other essential industries. Through our vertically integrated capabilities, including scalable additive manufacturing and precision machining, we deliver true end-to-end production at scale by combining cutting-edge technology with a foundation of proven industrial craftsmanship.

Our team brings deep expertise in manufacturing, materials science, software, automation, and operations, united by a shared commitment to quality, curiosity, and accountability. We foster an inclusive culture where people take ownership, solve meaningful problems, and continuously learn, because we believe diverse perspectives lead to better outcomes.

The Senior Computer Vision Engineer designs and deploys vision-based software systems that enable advanced process monitoring, inspection, and quality assurance across Vulcan Forms’ additive manufacturing operations. This role focuses on developing and integrating computer vision and machine learning solutions that analyze image and video data collected throughout the manufacturing process to drive real-time insight, automation, and process control.

By combining algorithm development expertise, deep knowledge of image analysis and machine learning, and production-quality software engineering practices, the Senior Computer Vision Engineer accelerates the adoption of vision-driven manufacturing technologies and improves factory performance, product quality, and operational efficiency.

• Architect, develop, and deploy computer vision systems for in-situ and ex-situ defect detection, inspection, and quality assurance across additive manufacturing workflows.
• Research, evaluate, and implement state-of-the-art computer vision and machine learning techniques—including segmentation, object detection, tracking, and anomaly detection—to identify defects and process deviations.
• Improve the accuracy, performance, and robustness of image processing and vision algorithms to deliver timely feedback that enables data-driven operational decisions.
• Preprocess, curate, and annotate large-scale image and video datasets to support model training, validation, and deployment.
• Establish performance metrics, conduct benchmarking, and continuously improve vision models and supporting software for reliability in production environments.
• Work closely with Process Monitoring, Process Engineering, Materials Science, Data Engineering, Quality, and Operations teams to scope requirements and deliver vision solutions that integrate seamlessly into factory systems.
• Ensure computer vision systems meet production standards through robust testing, monitoring, version control, and deployment practices.
• Serve as a technical expert for stakeholders adopting vision-based tools, providing guidance on capabilities, limitations, and opportunities for improvement.

• 5+ years of experience in computer vision, machine learning, or applied software engineering roles.
• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, Applied Mathematics, or a related field (or equivalent experience).
• Strong proficiency in Python and experience with computer vision and ML frameworks (e.g., OpenCV, PyTorch, Tensor Flow).
• Proven experience developing and deploying vision algorithms for real-world or production environments, especially utilizing GPUs for faster computation.
• Strong understanding of image processing, feature extraction, segmentation, and object detection techniques.
• Experience working with large-scale image and video datasets, including data preprocessing and annotation workflows.
• Solid software engineering fundamentals, including version control, testing, and CI/CD practices.
• Ability to independently own technical solutions and collaborate effectively across multidisciplinary teams.