Computer Vision Engineer

Summary

Join RYZ Labs as a highly skilled Computer Vision Engineer and collaborate on cutting-edge AI solutions transforming the global supply chain. You will develop AI-powered vision systems for a forward-thinking client, focusing on intelligent automation and real-time insights using computer vision, OCR, and deep learning. This remote position, based in Argentina or Uruguay, offers the chance to apply your expertise to high-impact challenges in a rapidly evolving industry. You will work with a team of great professionals and specialists in a supportive and challenging environment. RYZ Labs is a startup studio with a focus on building industry-defining companies. The company values a customer-first mentality, bias for action, ownership, humility and respect, frugality, delivering impact, and raising standards.

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

Responsibilities

• 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

Preferred Qualifications

• Experience with tracking objects across multiple cameras for persistent identification
• Knowledge of coordinating PTZ (pan-tilt-zoom) cameras for automated tracking
• Experience deploying and optimizing models on edge hardware
• Familiarity with cloud-based machine learning workflows (Google Cloud, AWS, or Azure)

Benefits

• Remote work
• Opportunities for learning and growth
• Challenging projects