Video Data Labeling Specialist (Remote | $50$90/hr) (WFH)

Video Data Labeling Specialist (Remote | $50$90/hr)

Start Date

Starts  Immediately

CTC (ANNUAL)

Competitive salary Competitive salary

Experience

1 year(s)

1 year(s)

Apply By

Not Provided

Posted few hours ago

Job

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About the job

Video Annotation Specialist

Position: Video Annotation Specialist

Type: Contractor

Compensation: $50$90/hour

Location: Remote

About the Opportunity

micro1 is engaging skilled Video Annotation Specialists to support an AI training project focused on reviewing and annotating videos of robotic arms performing designated tasks. Youll apply strong visual analysis, attention to detail, and annotation expertise to help create high-quality training data for next-generation AI systems. No prior AI experience is required your ability to accurately review and annotate real-world video data is what matters.

Responsibilities

• Review video footage of robotic arms performing assigned tasks and identify key actions and outcomes.
• Apply detailed grading guidelines to accurately tag and annotate events within video sessions.
• Use video annotation tools and platforms to create structured observations and labeled datasets.
• Identify relevant actions, events, and outcomes with a high degree of accuracy.
• Collaborate with project trainers and other contributors to resolve ambiguous cases and maintain annotation consistency.
• Document findings and submit annotated datasets according to project standards and milestones.
• Communicate project progress, challenges, and relevant insights through written and verbal channels.
• Participate in ongoing quality reviews and incorporate feedback to improve annotation accuracy.
• Maintain consistent annotation standards across assigned video sessions.
• Manage workloads independently while meeting project deadlines.

Required Qualifications

• Experience with video annotation, data labeling, or similar annotation projects .
• Exceptional attention to detail and accuracy when reviewing visual data.
• Strong written and verbal communication skills.
• Demonstrated time-management and self-organization abilities.
• Familiarity with video annotation tools and software platforms.
• Ability to follow detailed grading and annotation guidelines consistently.
• Strong analytical and problem-solving skills.
• Ability to work independently while collaborating effectively with project teams.

Preferred Qualifications

• Background in AI training or machine learning data preparation .
• Experience working on robotics-related projects .
• Previous participation in AI training, data labeling, or model evaluation projects.
• Strong ability to identify and resolve ambiguous annotation scenarios.
• Proactive approach to improving annotation quality and consistency.
• Familiarity with structured visual-data workflows.
• Experience working with high-volume annotation projects.

Compensation

• $50$90 per hour
• Remote contractor engagement.
• Large-scale project with 10,000 openings .
• No prior AI experience required.

Eligibility

• Applicants should have relevant experience in video annotation, data labeling, or comparable visual-data projects.
• Candidates must be able to work independently, follow detailed guidelines, and meet project milestones.

Application Process

1. Apply for the Video Annotation Specialist opportunity.
2. Highlight relevant experience in video annotation, data labeling, or AI training projects.
3. Demonstrate familiarity with video annotation tools and structured data-labeling workflows.
4. Complete any project-specific evaluation or onboarding requirements provided by micro1.
5. Begin reviewing and annotating robotic-arm video data according to the provided grading guidelines.

Earn certifications in these skills

Who can apply

Only those candidates can apply who:

1. have minimum 1 years of experience

Salary

Probation:

Duration:

Salary during probation:

After probation:

Annual CTC: Competitive salary

Number of openings

1

Editor’s note

Information above is Internshala's interpretation and paraphrasing of what we found on the shared link.

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