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Face Deduplication Job at TELUS Digital – Apply Now

Kingsley E.O
Kingsley E.O
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Face Deduplication Collection Job at TELUS Digital

Face Deduplication Collection Job at TELUS Digital
Face Deduplication Job at TELUS Digital – Apply Now

TELUS Digital hires remote workers for a face deduplication collection project. You help build datasets for machine learning and facial recognition research. This freelance role pays $0.55 USD for each accepted image. You need no special degree or experience. The World Economic Forum reports AI and big data skills rank among the fastest growing in the global job market.

World Economic Forum Future of Jobs Report 2025 confirms the demand. Remote data work offers a real entry point into the AI economy. You earn money from home while supporting projects used by technology companies.

Face Deduplication Collection

You submit selfies and older photos to support TELUS Digital machine learning research. Your images help train facial recognition models to handle real-world changes in age, lighting, and pose. This task-based freelance role suits anyone with a smartphone and a gallery of personal photos.

Responsibilities

  • Submit current neutral selfies as clean frontal reference images.
  • Capture head-pose selfies in assigned directions.
  • Provide older historical images from your personal gallery.
  • Follow project guidelines for lighting, background, and facial visibility.
  • Submit at least 30 images to reduce quality control rejections.
  • Respond to feedback and replace rejected images when needed.

Qualifications

  • A smartphone with a working front camera.
  • A Gmail address for your primary account.
  • Access to older personal photos for historical image tasks.
  • Clear facial images without heavy filters or editing.
  • Attention to detail and consistent instruction following.
  • No formal education requirement.

Each accepted image joins a larger dataset used to train and test computer vision models. Your submissions improve model accuracy across different ages, poses, and lighting conditions. TELUS Digital uses these real-world variations to reduce bias in facial recognition systems.

About TELUS Digital

TELUS Digital grew from TELUS, a Canadian communications company. The business launched as TELUS International in 2005 and rebranded as TELUS Digital in 2024. Today the company operates a managed AI community with more than one million contributors around the world.

The company provides data collection, labeling, and validation services for generative AI, computer vision, and natural language processing. TELUS Digital works with global technology brands to improve smart devices, search results, speech recognition, and bot interactions. The company values accuracy, privacy, and human intelligence.

Remote freelance roles give you the freedom to work from home and set your own schedule. You join a global network of annotators and linguists who help companies test and improve machine learning models. The culture focuses on quality and inclusion. Workers receive clear project instructions and quality feedback. Many contributors start with small task-based projects and move into longer term roles.

Learn more on the TELUS Digital official website.

Salary and Benefits

Pay for this project is $0.55 USD per accepted image. You must submit a minimum of 20 valid images to qualify for payment. The maximum payout covers 24 accepted images, for a total of $13.20 USD. TELUS Digital recommends you submit 30 images because automated and manual quality control will reject some entries.

Your actual earnings depend on how many images pass review. This is a freelance task-based role, not a salaried position. Benefits include remote work, flexible hours, and no commute. You choose when to complete the work. TELUS Digital does not require a degree for this project. You need only a smartphone and a Gmail account. The company often offers access to other paid projects inside its AI community.

Career Growth and Industry Outlook

The AI data collection field is growing quickly. Grand View Research reports the global data annotation and labeling market continues to expand as AI models demand more real-world training data. The World Economic Forum found AI and big data skills rank among the fastest growing by 2030.

This project builds hands-on experience with computer vision data pipelines. You learn how datasets support facial recognition model training. You also practice following strict quality control standards. These skills transfer to roles like data annotator, quality control reviewer, and AI project coordinator.

TELUS Digital often offers additional freelance tasks after you complete one project. Some workers move into specialized roles in machine learning operations or data labeling leadership. If you want a longer term technical path, explore roles like machine learning engineering manager or senior software engineer. For business side roles, see product manager marketing.

Data annotation market research shows steady growth. This trend creates more remote work for people who understand data quality. You start with image collection and build a foundation for other AI-related tasks.

How to Apply and Job Tips

Follow these steps to improve your chances. Use a Gmail address as your primary account. This is a firm project requirement. Submit 30 images, not the minimum 20. Extra images give you a buffer after quality control. Use natural daylight for clear selfies. Avoid heavy filters, hats, sunglasses, and dark shadows. Keep your face fully visible and centered. For historical images, choose original photos from your gallery. Do not use screenshots of other photos. Check each image before uploading. Blurry or duplicate images will fail review.

Before you apply, review this guide on how to find legitimate jobs online. If you need help with your application materials, review this resume tips guide and this interview preparation resource. Build a professional online presence with LinkedIn profile tips. Strengthen your candidate profile with soft skills employers look for. Use networking tips to connect with AI community workers.

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Glossary

  • Face deduplication: Removing duplicate or near-duplicate facial images from a dataset.
  • Neutral selfie: A front-facing photo with a calm expression and no heavy styling.
  • Head-pose capture: A selfie taken at assigned head angles to add pose variation.
  • Historical images: Older personal photos showing natural aging and long-term appearance changes.
  • Quality control: Automated and manual review that checks each image against project rules.
  • Machine learning: Computer systems learning from data to make predictions or decisions.
  • Computer vision: Field of AI that helps machines interpret images and video.
  • Dataset: A collection of images or data used for training machine learning models.

Frequently Asked Questions

What is the Face Deduplication Collection project?

This project collects neutral selfies, head-pose captures, and historical facial images. TELUS Digital uses these images to train and test facial recognition models.

How much will I earn?

You earn $0.55 USD for each accepted image. The maximum payout covers 24 accepted images, for $13.20 USD total. You need 20 valid images to qualify for payment.

Do I need a Gmail account?

Yes. Use a Gmail address as your primary account when you apply for this project.

What images do I need to submit?

You submit current neutral selfies, head-pose selfies in assigned directions, and older photos from your personal gallery.

What happens if my images fail quality control?

Rejected images do not count toward your payout. Submit 30 images to leave room after automated and manual reviews.

Is this a full time job?

No. This is a freelance task-based role. You work from home and choose your own schedule.

How do I apply?

Use a Gmail account and follow the project instructions from TELUS Digital. Click the apply button below to start.

CLICK TO APPLY
Kingsley E.O
Kingsley E.O Ezeme Kingsley Onyebuchi is an education writer and scholarship researcher who turns personal scholarship experience into practical guidance for students worldwide. As a recipient of three fully funded engineering scholarships, he combines firsthand experience with careful research to help students find credible admissions, scholarship, and fellowship opportunities.

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