TELUS AI Data Collection Task - Entry-Level ML Gig in Columbus
Career & Market Insight
This is a micro-task / crowdsourced data labeling gig for TELUS's machine learning team — not a career-track role. It sits in the growing field of AI training data collection, which platforms like Scale AI, Appen, and TELUS International rely on heavily.
Career relevance: While this specific task requires no skills and offers no career progression, it touches on an important and fast-growing area: facial recognition model training and AI dataset curation. For those curious about the data side of ML, participating gives a ground-level view of how training datasets are constructed.
- No education required — this is open to anyone.
- No skills are developed or demonstrated through this task.
- Payment is contingent on quality control approval — a reminder that even micro-tasks have professional standards.
Red flags for career seekers: This is not a job in the traditional sense. There is no salary, no benefits, no growth path, and no employer relationship. Compensation is task-based and capped at 24 accepted images worth of payment.
Green flag: If you are exploring the AI data marketplace (Appen, Lionbridge, TELUS International), this type of task is a low-barrier entry point to understand how crowdsourced AI data projects work — useful context for roles in ML operations or data quality.
The objective of this project is to collect a large and diverse dataset of current neutral selfies, head-pose captures, and historical facial images to support machine-learning research and facial recognition model training at TELUS.
The focus is on capturing real-world variation across lighting, poses, expressions, accessories, environments, and aging to improve model accuracy and robustness. The collection includes:
- Current Neutral Selfies – clean frontal selfies serving as high-quality identity references, with natural variation in appearance and surroundings.
- Current Head-Pose Captures – selfies captured in assigned head directions to introduce pose variation.
- Historical Images – older photos from participants' galleries to capture natural aging and long-term appearance changes.
To qualify for payment, you must submit a minimum of 20 valid images. The maximum payout is based on 24 accepted images. Due to the strict automated and manual Quality Control (QC) process, we strongly recommend submitting 30 images to help ensure that enough images remain valid after review.
Note: Please use a Gmail address as your primary account when applying for this project.
Qualification path: No specific education is needed to perform the project task.
Frequently Asked Questions
How many images do I need to submit to get paid?
Do I need any qualifications or experience to participate?
Can I complete this task remotely from any location?
What email address do I need to apply?
What types of images are required for this project?
