We are seeking a Computer Vision and Machine Learning Expert to evaluate and improve AI systems that process images, videos, screenshots, documents, and multimodal inputs. The role involves reviewing AI-generated visual responses, identifying hallucinations or reasoning errors, evaluating model outputs, analyzing dataset quality, improving annotation guidelines, and providing structured feedback to enhance model accuracy and reliability. The ideal candidate has strong knowledge of computer vision, machine learning, deep learning, image classification, object detection, segmentation, OCR, visual question answering, image captioning, and multimodal AI. They should understand evaluation metrics such as precision, recall, F1-score, IoU, mAP, confusion matrices, false positives, false negatives, and threshold tuning. Required skills include Python, PyTorch or TensorFlow, OpenCV, Hugging Face, dataset analysis, annotation quality review, model evaluation, and clear technical communication. Experience with YOLO, CNNs, Vision Transformers, CLIP, SAM, ResNet, Mask R-CNN, or similar models is preferred. The interview should assess the candidate’s ability to explain computer vision projects, evaluate model performance, identify data and annotation issues, compare AI-generated image responses, handle edge cases, and recommend practical improvements for visual AI systems.