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Computer Vision Engineer

Continental · He Fei Shi, An Hui Sheng, cn

Spotted 14d agoAssociateFull-time

Job details

Employment
Full-time
Posted
Oct 13, 2026
Last confirmed open
Oct 27, 2026

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Job description

About this role

  • Design, develop, deploy, and maintain computer vision solutions for automated inspection, defect detection, quality assurance, process monitoring, and manufacturing intelligence.
  • Integrate vision applications with industrial cameras, lighting, controls, manufacturing execution environments, databases, and edge-computing platforms.
  • Lead or support validation, commissioning, production rollout, user enablement, and operational support for plant-wide vision use cases.
  • Monitor production performance and improve model robustness under changing products, equipment conditions, lighting, and process variation.
  • Diagnose image-acquisition, camera-calibration, equipment-drift, and model-performance issues and drive sustainable corrective actions.
  • Identify and prioritize new industrial vision opportunities, translating manufacturing pain points into deployable use cases with measurable operational impact.
  • Evaluate emerging technologies such as deep-learning vision, multimodal AI, foundation vision models, and industrial vision platforms.
  • Apply appropriate data, cybersecurity, model-risk, and responsible-AI controls throughout the solution lifecycle.

Computer vision and image processing, including model development, validation, performance measurement, and deployment.

  • Python and relevant AI / vision frameworks; practical software deployment skills. Additional industrial programming capability is advantageous. Confidential | Prepared for compensation benchmarking purposes only
  • Industrial cameras, optics, lighting, calibration, sensors, and edge-computing concepts.
  • Industrial integration concepts, including PLC / controls, manufacturing systems, databases, APIs, and common connectivity protocols.
  • Data analysis and visualization; structured problem solving using quality and root-cause methods.
  • Project management, stakeholder communication, documentation, and change adoption in an operations environment.

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