Video Data Annotation- Intern

Video Data Annotation Specialist – Forensic Analysis

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Experience Level:

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About the Role

Video Data Annotation Specialist

AI-driven video investigation projects

Key Responsibilities

• Perform

  frame-level and sequence-level annotation

  for forensic video datasets, including:
• Face and person tracking
• Object and vehicle labeling
• Action tagging 
• Scene change detection and event segmentation
• Identify and tag

  tampering cues

  , such as missing frames, splices, or compression artifacts.
• Configure and manage

  annotation tools and pipelines

  (CVAT, Supervisely, V7, or internal forensic labeling systems).
• Maintain strict

  data confidentiality and chain-of-custody documentation

  .
• Define

  annotation guidelines

  specific to forensic evidence handling.
• Conduct

  quality audits

  and maintain >95% accuracy and inter-annotator consistency.
• Collaborate with forensic analysts and AI researchers to refine model training datasets.
• Generate

  summary reports

  of annotated findings for internal documentation and validation.

Requirements

• Prior exposure to

  forensic or investigative video datasets

  (law enforcement, cybercrime, or lab projects).
• Proficient in

  CVAT, Label Studio, Supervisely, V7 Darwin,

  or equivalent tools.
• Understanding of

  video formats, compression, metadata, and temporal labeling

  .
• Familiarity with

  forensic media standards

  (e.g., chain of custody, evidence preservation).
• Basic scripting knowledge in

  Python, OpenCV, or FFmpeg

  for preprocessing and frame extraction.
• Excellent attention to visual and temporal details; high data accuracy tolerance.
• Ability to work under confidentiality protocols and handle sensitive evidence.

Preferred Qualifications

• Experience in

  face or gait analysis datasets

  , or

  deepfake/tampering detection

  annotation.
• Exposure to

  AI-based forensic tools

  or

  ML dataset preparation

  pipelines.
• Prior work in a

  government forensic lab, defense, or digital investigation project

  .
• Knowledge of

  image forensics metrics

  (e.g., PRNU, ELA, or compression-based indicators).