Machine Vision Engineer

As a Machine Vision Engineer, you will leverage your expertise to train next-generation AI systems, significantly influencing how models learn, reason, and perform through high-quality, real-world input. Your domain knowledge is crucial, and no prior experience in AI is required.

• Design and develop robust machine vision algorithms for image analysis and automated inspection tasks.
• Utilize OpenCV and other computer vision libraries to build scalable solutions that integrate seamlessly with existing systems.
• Optimize image processing pipelines for accuracy, efficiency, and reliability across diverse data sources.
• Collaborate with cross-functional teams to define system requirements and deliver project milestones.
• Analyze, preprocess, and annotate large datasets to train, validate, and test vision models.
• Troubleshoot and resolve complex computer vision challenges, ensuring consistent high-quality results.
• Document technical processes and actively contribute to internal knowledge sharing.

• Proven expertise in computer vision, image processing, and machine vision systems.
• Extensive hands-on experience with OpenCV and similar libraries.
• Strong programming proficiency in Python and/or C++ for vision applications.
• Solid understanding of camera hardware, imaging sensors, and lighting principles.
• Exceptional written and verbal communication skills, with the ability to clearly articulate complex concepts.
• Demonstrated ability to work independently and manage multiple priorities in a remote setup.
• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.

• Experience developing and deploying vision solutions in manufacturing, robotics, or similar domains.
• Familiarity with deep learning frameworks for image analysis (e.g., TensorFlow, PyTorch).
• Background in optimizing real-time computer vision systems for embedded devices.

Employment Type: Contract

Hourly rate ranges from $30 to $90.

This position is remote and open to qualified candidates regardless of location.