Description review
AI Data Annotation Expert
micro1 · Remote · back to the listing
HR standards
68/100
needs work
Title ↔ description
75/100
solid
Reads as
Unclear
no confident match
What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Role Title: AI Data Annotation Expert
Role Type: Contractor
Location: Remote
micro1 is selecting AI Data Annotation Experts to contribute to a client-driven project focused on improving AI systems through meticulous video data labeling and annotation. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
Scope of Work
• Review short video clips to identify and categorize recurring events according to provided guidelines.
• Precisely annotate each recognized event, capturing accurate timestamps for every occurrence.
• Craft clear, concise, and consistent written descriptions for all annotated events.
• Utilize attention to detail and data labeling accuracy to ensure high-quality, reliable annotations.
• Apply annotation protocols rigorously to maintain consistency and integrity of the labeled data.
• Engage collaboratively with project leads and fellow contributors to address questions and feedback.
• Submit regular deliverables aligned with project milestones and expectations.
Preferred Qualifications
• Proven experience with video annotation or similar data labeling activities.
• Exceptional attention to detail and a commitment to maintaining data quality.
• Strong analytical skills, with the ability to interpret guidelines and apply them thoroughly.
• Excellent written and verbal English proficiency for clear annotation descriptions and communication.
• Experience participating in AI training or data-centric projects is advantageous.
• Demonstrated ability to collaborate effectively within distributed teams or remote projects.
• Comfort working independently and adapting to evolving annotation criteria or feedback.
Originally posted on Himalayas
Role Type: Contractor
Location: Remote
micro1 is selecting AI Data Annotation Experts to contribute to a client-driven project focused on improving AI systems through meticulous video data labeling and annotation. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
Scope of Work
• Review short video clips to identify and categorize recurring events according to provided guidelines.
• Precisely annotate each recognized event, capturing accurate timestamps for every occurrence.
• Craft clear, concise, and consistent written descriptions for all annotated events.
• Utilize attention to detail and data labeling accuracy to ensure high-quality, reliable annotations.
• Apply annotation protocols rigorously to maintain consistency and integrity of the labeled data.
• Engage collaboratively with project leads and fellow contributors to address questions and feedback.
• Submit regular deliverables aligned with project milestones and expectations.
Preferred Qualifications
• Proven experience with video annotation or similar data labeling activities.
• Exceptional attention to detail and a commitment to maintaining data quality.
• Strong analytical skills, with the ability to interpret guidelines and apply them thoroughly.
• Excellent written and verbal English proficiency for clear annotation descriptions and communication.
• Experience participating in AI training or data-centric projects is advantageous.
• Demonstrated ability to collaborate effectively within distributed teams or remote projects.
• Comfort working independently and adapting to evolving annotation criteria or feedback.
Originally posted on Himalayas