Senior AI Trainer

What you would do

• Review video footage of robotic arms performing assigned tasks, identifying critical actions and outcomes
• Apply detailed grading guidelines to tag events, transitions, and task boundaries with structured annotations
• Mark frame-accurate timestamps demarcating the start and end points of each action within videos
• Verify timestamp accuracy and consistency across all labeled content, correcting misalignment issues
• Collaborate with project leads to resolve ambiguous cases and refine annotation standards

Who they want

• Background in annotating videos, tagging data, or equivalent work
• Familiarity with timeline-based annotation software and frame-level or timecode-based marking
• Exceptional attention to detail, accuracy, and analytical problem-solving skills
• Strong written and verbal communication for remote collaboration and status updates
• Background in AI training, machine learning preparation, or robotics projects (strongly preferred)

Main skills

What the interview asks about

1. 1.Timestamp precision and verification

   Model training accuracy depends directly on frame-level label correctness; timestamp drift across a dataset compounds into training failures.

   For example: “You're marking a 15-frame arm motion in a 30fps video. How would you verify your start and end timestamps are frame-accurate, and what tool features help catch off-by-one errors?”

2. 2.Guideline interpretation under ambiguity

   Real-world footage contains boundary cases that written guidelines don't fully address; inconsistent interpretation compromises dataset quality.

   For example: “Guidelines state to mark when 'the arm stops moving.' A smooth curve slows over 8 frames. Which frame would you mark, and how would you document this decision for consistency?”

3. 3.Quality review and error detection

   Self-review catches errors before submission, reducing reviewer rework and maintaining dataset integrity across thousands of labels.

   For example: “Quality review flags 3 timestamp misalignments across 50 videos. Walk through identifying the root cause - tool drift, fatigue, or misunderstood guidelines - and preventing it in the next batch.”

4. 4.Annotation software efficiency

   Fluency with timeline interfaces, playback controls, and marking shortcuts reduces annotation time while preserving accuracy.

   For example: “Your annotation tool lets you loop a 2-second region, set hotkeys, and rewind frame-by-frame. Describe your workflow for marking a 12-frame transition with 1-frame precision while maintaining speed across 100 similar clips.”

5. 5.Collaboration and communication

   Remote projects depend on clear escalation of ambiguities and proactive communication about edge cases or process improvements.

   For example: “Five clips show task completion states not described in documentation. How would you handle this? What would you communicate to the team, and how would you proceed while waiting for clarification?”

A task you may get

Annotate a 2-3 minute video clip of a robotic or mechanical process using a simple timeline interface, marking task boundaries and transitions with frame-level precision. Verify your annotations for consistency and describe how you'd catch potential errors.

How to prepare

• Practice using free video annotation tools like CVAT or Supervisely to build familiarity with timeline interfaces and timecode entry
• Study video frame rates, timecode formats (SMPTE), and how to calculate frame numbers from timestamps
• Prepare examples of detailed annotation guidelines you've followed and discuss how you resolved ambiguous cases
• Review a robotics or computer vision paper to understand how annotation quality affects model performance

The facts

Pay

$50–90/hr

Open to

Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia

Field

Ai Machine Learning

Role type

Generalist

Posted

9/10/2026

Places left

1000

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