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Open nowPosted 3 days ago

Freelance Quality Check Reviewer – Video Data (Onsite, Graveyard Shift, Davao City)

careerflow140 open roles

Pay
₱20,000 – ₱25,000 a monthly
Where
Davao, Philippines
Work mode
On site
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Your applicationOpen nowFreelance Quality Check Reviewer – Video Data (Onsite, Graveyard Shift, Davao City)careerflow · Davao, Philippines

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  6. 58.1%60 days
This job: posted 3 days ago

The posting

About Careerflow.ai Human Data Labs

Careerflow.ai Human Data Labs is the data operations arm of Careerflow.ai, an AI-powered platform that helps people manage their job search with tools for resume optimization, application tracking, LinkedIn enhancement, and career coaching. Human Data Labs partners with AI and robotics companies to deliver high-quality, human-generated training data, creating freelance and project-based opportunities for our community.

Role Overview

Careerflow.ai Human Data Labs is hiring freelance Quality Check Reviewers for a project-based engagement supporting robotics and embodied AI data collection. Reviewers evaluate first-person household task videos recorded by remote contributors, checking that each submission meets project quality standards before it is accepted into the training dataset.

We're looking for reliable, detail-oriented reviewers who can apply strict quality guidelines consistently, work efficiently through a steady queue of submissions, and give clear, actionable feedback.

Engagement Details

  • Engagement type: Freelance, project-based. This is not a regular or full-time employment position; the engagement runs for the duration of the project.
  • Work setup: Onsite at our Davao City location. Remote work is not available.
  • Schedule: Graveyard shift, 12:00 MN – 9:00 AM PHT.
  • Equipment: Reviewers bring and use their own laptop or desktop computer onsite.

Compensation

Competitive monthly pay, plus:

  • Graveyard shift allowance
  • Transportation allowance
  • Device allowance
  • Monthly performance bonus based on accuracy and productivity

Full compensation details will be discussed during the interview.

What You'll Do

  • Review first-person video recordings of household tasks such as cleaning, folding laundry, organizing items, and food preparation.
  • Check each submission against detailed quality protocols covering framing, camera stability, lighting, hand-object visibility, task completeness, file naming, and submission requirements.
  • Approve, reject, or flag recordings for rework, and document the reason for each decision clearly and accurately.
  • Write concise feedback that helps contributors fix recurring issues and improve future submissions.
  • Track review outcomes and report recurring quality problems, unclear guidelines, or tooling issues to the project lead.
  • Meet daily and weekly review targets while maintaining high accuracy.

Required Equipment (Provided by You)

  • Your own laptop or desktop computer capable of smooth 1080p video playback.
  • Willingness to bring and use your own device onsite for every shift.

Required Qualifications

  • Availability to work the graveyard shift (12:00 MN – 9:00 AM) onsite in Davao City for the full project duration.
  • Strong attention to detail and the ability to follow SOPs and quality guidelines precisely.
  • Comfort working with video players, spreadsheets, web-based review tools, and cloud storage platforms.
  • Consistent judgment across a high volume of repetitive review tasks.
  • Clear written English for logging decisions and giving contributor feedback.
  • Reliable attendance and punctuality for onsite shifts.

Preferred Qualifications

  • Prior experience in data annotation, content moderation, QA, or video review.
  • Familiarity with AI training data, robotics, or embodied AI projects.
  • Experience working against defined productivity and accuracy targets.
  • Prior experience working night or graveyard shifts.

Success in This Role

Strong reviewers make fast, accurate, and consistent decisions that keep low-quality data out of the dataset and help contributors improve over time. Their work ensures robotics systems are trained on clean, reliable real-world footage of how people interact with objects in their homes.

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