Ryz Labs
Ryz Labs builds startups from the ground-up and helps other startups scale by providing top-tier technical talent solutions.
Startups • Technology • Software Development
11 - 50 employees
Computer Vision Engineer
March 7
Ryz Labs
Ryz Labs builds startups from the ground-up and helps other startups scale by providing top-tier technical talent solutions.
Startups • Technology • Software Development
11 - 50 employees
📋 Description
• Remote position within Argentina or Uruguay • RYZ Labs is looking for a highly skilled Computer Vision Engineer to collaborate on cutting-edge AI solutions that transform the global supply chain. • You’ll work with a forward-thinking client to develop AI-powered vision systems that analyze real-world logistics operations, enabling intelligent automation, real-time insights, and advanced object recognition through computer vision, OCR, and deep learning. • This is a unique opportunity to apply your expertise in AI and machine learning to solve high-impact challenges in a rapidly evolving industry. • Develop and optimize computer vision models for real-time vehicle recognition, OCR, and object tracking. • Design and implement hybrid edge + cloud architectures for scalable, low-latency inference. • Train and deploy YOLO-based object detection models in real-world applications. • Build active learning workflows to continuously improve model accuracy. • Use OpenCV and deep learning frameworks to process and extract structured data from images and video. • Optimize model performance for deployment on edge devices and cloud environments. • Work with software engineers to integrate models into scalable production systems. • Experiment with new computer vision techniques to push the boundaries of automation in logistics.
🎯 Requirements
• Strong proficiency in Python and deep learning frameworks like PyTorch. • Hands-on experience with YOLO (v8, v11, or similar) for object detection. • Experience with OpenCV for image processing and feature extraction. • Practical knowledge of OCR technologies such as Tesseract, EasyOCR, or commercial APIs. • Understanding of model training, evaluation, and optimization for real-world deployment. • Familiarity with active learning workflows to improve model performance over time. • Experience with hybrid edge + cloud architectures for deploying vision models. • Strong problem-solving skills and ability to work in a fast-paced, evolving environment.
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