We are seeking detail-oriented AI Annotators to support the development of secure and fair identity verification systems. Your work will involve labeling and validating datasets used in document fraud detection, deepfake detection, presentation attack detection (PAD), and face recognition. The annotations you provide will directly improve model accuracy, fairness, and resilience.

Responsibilities:
Data annotation across multiple formats (documents, images, video, audio) following project guidelines.
Identify and label document elements (e.g., names, photos, signatures, holograms) and flag potential tampering.
Annotate manipulated regions in deepfake and image tamper datasets.
Tag liveness indicators and spoof artifacts in PAD datasets.
Draw bounding boxes, facial landmarks, and expression labels for face recognition tasks.
Apply time-based labels in video for frame-level PAD/deepfake detection.




Document edge cases and suggest improvements to annotation guidelines.
Review peer annotations, check consistency, and ensure quality standards.
Collaborate with ML engineers and data scientists to improve dataset quality and tools.

Requirements:
Attention to detail and robust observational skills.
Ability to focus on repetitive tasks while maintaining accuracy.
Basic understanding of computer vision/ML concepts (training provided).
Comfortable using web-based annotation tools (training provided).
Transparent written and verbal communication skills to describe edge cases.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Analyst I Tamil Nadu
🏢 Elsevier
📍 Tamil Nadu