The work
As an Image/Video Annotator, you will review static images and short video clips to help AI systems understand visual content, motion, and context. You will apply detailed guidelines, make structured judgments, and check AI-generated results for accuracy and relevance.
The work includes describing what happens across a sequence, including scene changes, interactions, mood, lighting, and spatial relationships. You will also coordinate with project leads as annotation standards and quality measures change.
- Analyze images and short videos for subjects, settings, actions, emotions, and narrative elements.
- Describe temporal sequences, scene transitions, interactions, mood, lighting, and spatial relationships.
- Follow detailed annotation guidelines to maintain consistent data quality.
- Validate AI-generated outputs for correctness, structure, and relevance.
- Contribute structured visual datasets that support multimodal AI development.
What it pays and takes
This is an entry-level, part-time contractor role open worldwide. Previous image or video annotation experience can help, but it is not required.
- Pay: Not specified in the supplied role details.
- Time: 20 or more hours per week.
- Location: Open worldwide for remote work.
- Language: Strong English reading, writing, communication, and comprehension skills.
- Equipment: A reliable desktop or laptop and stable internet connection.
- Skills: Careful attention to detail, analytical thinking, and the ability to follow nuanced instructions.
- Tools: Comfort with basic computer tools, file uploads, and audio-related file formats.
- Work style: Self-motivated, independent, and able to communicate and collaborate remotely.
How it works
Create or use your free OpenTrain account and apply with your resume. You will then complete the application on the hiring site.
About AI training work
AI training is the human work behind artificial intelligence, including labeling images and videos and reviewing model outputs. People who bring careful judgment and strong attention to detail help models interpret information more accurately.