Computer Vision Engineer

About the role

This role sits within a digital innovation and manufacturing IoT group focused on developing and deploying advanced digital solutions that improve production efficiency, quality, and safety in a sustainable way.

Responsibilities

• Develop new digital technology solutions for manufacturing, including computer vision inspection systems and AI/machine learning analysis of machine and sensor data.
• Engage with regional plant-floor customers to understand their processes, identify improvement opportunities, and translate user and customer needs into technical solutions.
• Analyze manufacturing problems and align them with appropriate products, technologies, and architectures that deliver measurable value in efficiency, quality, and safety.
• Contribute to the definition and execution of the IoT strategy and roadmap for selected manufacturing domains and use cases.
• Monitor emerging digital and IoT technologies and assess their potential for future manufacturing projects and pilots.
• Support and implement analytical processes such as machine learning and advanced data analysis from proof-of-technology concepts through to deployed IoT projects.
• Extract, interpret, and utilize parameter data from machines to assess equipment health, verify proper operation, and understand impacts on product quality.
• Design, implement, and optimize computer vision algorithms, including segmentation, morphology, pose estimation, camera calibration, image enhancement, feature extraction, classification, 3D vision, and deep learning-based methods.
• Implement and integrate vision algorithms using tools such as C++, HALCON, Matrox, Cognex, and Keyence, leveraging both traditional rule-based approaches and deep learning techniques.
• Apply techniques such as vibration, audio, or current signature analysis to monitor machine condition and support predictive or prescriptive maintenance use cases.
• Collaborate with cross-functional teams, including controls engineers, data engineers, and plant operations personnel, to ensure successful deployment and adoption of IoT and vision solutions.
• Document solution designs, configurations, and best practices to support scalability, maintainability, and knowledge sharing across projects and sites.

Requirements

• Bachelor’s degree in engineering or an engineering-related field such as electrical engineering, controls, systems, computer science, data science, or equivalent experience.
• At least 2 years of experience in manufacturing equipment controls, network design, embedded systems, machine learning, and/or data engineering, preferably with a focus on the automotive industry.
• Strong understanding of manufacturing equipment controls and logic, with hands-on knowledge in at least one PLC platform such as Mitsubishi, Rockwell, Siemens, or Omron.
• Solid knowledge of computer vision algorithms, including segmentation, morphology, pose estimation, camera calibration, image enhancement, feature extraction, classification, 3D vision, and deep learning techniques.
• Experience implementing computer vision algorithms in C++ and tools such as HALCON, Matrox, Cognex, and/or Keyence, using both traditional rules-based and deep learning approaches.
• Experience with vibration, audio, or current signature analysis for assessing machine condition and performance.
• Demonstrated ability to support and apply machine learning and analytical processes within IoT and digital manufacturing projects.
• Ability to engage effectively with plant-floor users and stakeholders to gather requirements, explain technical concepts, and drive solution adoption.

Essential Skills

• Experience working with vision systems in industrial or manufacturing environments.
• Background or exposure to automotive manufacturing processes and standards.
• Familiarity with IoT architectures, connected devices, and edge-to-cloud data flows in a production setting.
• A creator mindset with curiosity about emerging digital technologies and a structured, risk-aware approach to deploying them in production.
• Strong problem-solving skills with the ability to translate complex manufacturing challenges into practical digital solutions.
• Effective communication and collaboration skills for working with cross-functional technical and operations teams.