The Role The Trainer will deliver advanced data annotation concepts covering multimodal datasets, LiDAR, video annotation, human-in-the-loop workflows, privacy, QA metrics and AI dataset preparation.

Responsibilities:

- Deliver advanced classroom and hands-on training on data annotation techniques, AI data pipelines, annotation standards, and industry best practices for creating high-quality datasets.

- Train students on advanced image and video annotation techniques, including polygon annotation, keypoint annotation, semantic segmentation, object tracking, and text and audio annotation using industry-standard tools.

- Conduct practical sessions on LiDAR, video, and multimodal sensor data annotation, enabling learners to work with datasets used in autonomous systems, robotics, and AI applications.





- Teach annotation quality assurance (QA), inter-annotator agreement metrics, data privacy, anonymization techniques, and human-in-the-loop workflows, ensuring adherence to industry quality standards.

- Mentor students in executing industry-oriented annotation projects, guiding them through dataset preparation, validation, quality review, and documentation to simulate real-world AI data operations.

Qualification:

- Master's degree in AI, Computer Science, Robotics or Data Science.

- 2-5 years' experience in AI data annotation.

- Experience with CVAT, Labelbox, SuperAnnotate or similar platforms.

📌 Trainer/Educator: Advanced Data Annotation (Mandi)
🏢 IIT Mandi iHub and HCI Foundation
📍 Mandi