📢 We’re Hiring: Image Proofreader (Onsite) Are you detail-oriented and passionate about ensuring data quality for AI? Join us as an Image Proofreader and play a critical role in developing and training Vision AI models used in healthcare, safety monitoring, autonomous systems, and more! Location: Onsite, Vadodara US (Pacific Time zones) What You’ll Do: Review and validate large image datasets for AI training Flag low-quality, corrupted, or irrelevant images Identify edge cases, labeling inconsistencies, and visual artifacts Collaborate with data annotation teams and ML engineers to ensure dataset quality Follow and improve visual QA guidelines What We’re Looking For: Strong attention to visual detail and ability to spot subtle inconsistencies Experience in image review, visual QA, or data annotation validation Familiarity with labeling platforms like CVAT, Supervisely, Labelbox, or similar Clear communication skills and structured feedback Availability to work onsite Nice-to-Haves: Prior experience supporting Vision AI or ML pipelines Knowledge of object detection, segmentation, or video frame QA Experience identifying edge cases and model failure modes Why Join Us? Work on real-world datasets powering production-grade AI models Contribute to impactful domains like healthcare, safety, and supply chain automation Onsite, collaborative, and supportive work environment Grow your career in AI operations, data QA, and computer vision If you’re passionate about quality, precision, and the intersection of human insight and machine learning, we’d love to hear from you! 📩 Apply today with your resume and any relevant experience with image datasets or annotation workflows.
🎯 Key Responsibilities Review image datasets to ensure visual quality, relevance, and annotation accuracy for Vision AI training Flag corrupted, low-quality, or irrelevant images that could impact model performance Identify edge cases, visual artifacts, labeling inconsistencies, or environmental variations in images Work closely with data annotation teams to verify labeling integrity and provide feedback for corrections Apply pre-defined visual QA guidelines and help refine those standards as the datasets evolve Collaborate with machine learning engineers and data managers to understand model requirements and edge-case scenarios Assist in curating high-quality training datasets for supervised learning ✅ Required Skills & Experience Strong attention to visual detail - ability to notice subtle inconsistencies across large volumes of image data Basic understanding of computer vision concepts and how image quality impacts model training Experience in image review, visual QA, data labeling, or annotation validation Familiarity with image review tools, labeling platforms, or dataset management systems (e.g., CVAT, Supervisely, Labelbox, or custom pipelines) Ability to follow structured review processes and meet quality benchmarks Clear communication skills to document issues and provide structured feedback Willingness to work as per US time zone (Pacific Time). 💡 Preferred Qualifications Prior experience supporting a Vision AI or ML training pipeline Understanding of object detection, classification, segmentation, or video frame QA Familiarity with edge-case review processes and model failure mode identification Background in data ops, annotation, labeling QA, or data curation kindly confirm if you would be interested and i can arrange next steps. Thanks