We are looking for a skilled Computer Vision&Robotics Engineer to design, develop, and deploy advanced vision-based solutions for real-world applications. You will work at the intersection of AI and robotics, building robust systems that operate reliably in production and real-time environments. Key Responsibilities Design, develop, and deploy computer vision models for real-world applications Build and optimize deep learning models for tasks such as object detection, segmentation, classification, tracking, and pose estimation Develop scalable image and video processing pipelines for both training and inference Deploy and optimize models for real-time and edge environments, ensuring low latency and high efficiency Integrate vision models into production systems, including automated and semi-autonomous platforms Collaborate with cross-functional teams (software, hardware, product) to deliver end-to-end solutions Evaluate model performance using real-world data and continuously improve accuracy, robustness, and efficiency Stay up to date with the latest advancements in computer vision, deep learning, and applied AI Required Qualifications Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field 2+ years of hands-on experience in computer vision and deep learning Strong programming skills in Python Experience with deep learning frameworks such as PyTorch or TensorFlow Solid understanding of core computer vision concepts (image processing, CNNs, feature extraction, object detection) Experience training and deploying machine learning models in production environments Familiarity with model optimization techniques (e.G., quantization, pruning, ONNX) Experience working with image/video datasets and data pipelines Strong analytical and problem-solving skills Preferred Qualifications Master’s degree in Computer Vision, Machine Learning, or a related field Experience with real-world or industrial AI applications Exposure to robotics or autonomous systems Familiarity with edge deployment or performance-constrained environments Experience with cloud-based ML infrastructure and MLOps workflows Nice to Have Experience with infrastructure robustness testing to ensure system stability and reliability Hands-on experience updating and validating software on physical robotic systems Exposure to real-world field testing and evaluating system performance in live environments Familiarity with end-to-end system validation, including testing under varying operating conditions