Generate a McCoy IQ challenge in 30 seconds.
See how candidates think and approach the work this role demands, before the phone screen. We'll build a video challenge from this posting, and you can edit or share it before it goes live.
Key details
Why this role is notable
A computer-vision vehicle-inspection company still relies on a large human workforce doing repetitive image labeling to train its models, showing how much frontier-adjacent AI work remains manual and largely invisible.
Job Description
Description
At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we pioneered the world's first fully automated suite of AI-powered vehicle inspection systems, combining computer vision, machine learning, and generative AI.
With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone.
We're looking for talented people for an entry-level position in the high-tech industry to join our Data Tagging Team.
This is a repetitive, detail-focused image review that needs sustained concentration and consistent accuracy.
A day in the life and how you’ll make an impact:
- Annotate and tag raw visual data and images collected by our inspection systems, following detailed guidelines
- Review and validate annotations as part of your regular routine
- Support our Machine Learning algorithms teams by delivering clean, consistent labeled data
- Your labels are the training data behind our models, so the accuracy of your work directly affects how well they perform.
Requirements
- Ability to work on repetitive tasks with accuracy and consistency, and to stay focused on them over time
- Close attention to detail and the ability to follow complex guidelines
- Completed Bachelor's degree (a relevant field is an advantage)
- Interest in building a long-term career and growing at UVeye
- Excellent verbal and written English
- Proficiency in Microsoft Office
- Technical orientation and good computer skills
- Ability to work independently and as part of a team
- Self-motivated, proactive, and a quick learner
Bonus if you have:
- Familiarity with the automotive market
- Relevant academic degree - advantage
Why UVeye:
- Pioneer Advanced Solutions: Harness cutting-edge technologies in AI, machine learning, and computer vision to revolutionize vehicle inspections.
- Drive Global Impact: Your innovations will play a crucial role in enhancing automotive safety and reliability, impacting lives and businesses on an international scale.
- Career Growth Opportunities: Participate in a journey of rapid development, surrounded by groundbreaking advancements and strategic industry partnerships.
- Work that matters: your labels are the training data behind our vehicle inspection models, so your accuracy shows up in how well they perform.
- Close to the technology: you will see how AI, machine learning and computer vision are applied to vehicle inspection, and support the teams that build it.
Interested in working for companies like this one?
Answer a McCoy challenge. Companies hiring in operations see your response and reach out when they want to talk.
About 15 minutes to prepare and 5 minutes to record. Unlimited retakes and a teleprompter help you answer. No AI training, and you can delete your response anytime.
This isn't an application to UVeye.
Audit details(provenance, verification trail, raw fields)
Core fields
uveye:A5.476Provenance
uveye|92.00F|29FA7C17971797FBA179753E53E0A7CVerification trail
This posting hasn't been probed by our closure verifier yet. Stream C runs on a rolling schedule against postings approaching the close-decision threshold.
LLM enrichment
See how we measure for definitions, or our corrections log for known issues. Found something wrong? Flag a correction.
