Role overview

Qualifications

Prior experience with CVAT
Comfortable working with video data across varied lighting and weather conditions
High tolerance for repetitive, detail-oriented work
Experience with YOLO-format datasets or prior computer vision annotation work is a strong plus

Responsibilities

Annotate video clips of intersections using CVAT
Review and correct AI-generated pre-labels
Review auto-generated bounding boxes across object classes
Apply tracking corrections and flag edge cases for engineer review

Key facts

Remote from: Latin America
Full time
English

Hard skills

CVSNT TrackWise

Other skills

Detail Oriented
Quality Assurance

About the company

NIR-YU

Human Resources, Staffing & Recruiting

Nir-Yu specializes in helping Small and Medium Enterprises unlock the potential of their business. Our typical customer is worried about not being able to make enough progress on their product(s) or project(s) with their current team; they need additional team-members to deliver work on-time and with the required quality. Tight budgets also make it necessary to lower average wage costs, making it necessary to consider hiring internationally to lower costs. We help these companies by providing a bespoke service that finds and hires remote workers from Mexico, Colombia, Argentina, Chile, Brazil and other countries in LatAm based on the company’s specific job descriptions to work as an extension of their team and perform the required functions. Unlike many other companies, we don’t have a bench of contractors to be hired out, instead each team is hired specifically to meet specific requirements and to work exclusively as a remote employee.

Company details

Company typeScaleup

IndustryHuman Resources, Staffing & Recruiting

Company size201 - 500

Links

Website LinkedIn See all jobs

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Job description

The Role:

We are building an edge AI system that monitors intersection safety in real time. We are expanding our detection capabilities and need a detail-oriented annotator to help us grow and maintain the training dataset that powers our models.

Responsibilities:

You will annotate video clips of intersections using CVAT, our annotation platform. The majority of your work involves reviewing and correcting AI-generated pre-labels rather than drawing from scratch — you are the quality layer, not the production layer. Day-to-day tasks include reviewing auto-generated bounding boxes across object classes (pedestrians, vehicles, and others), applying tracking corrections when objects are occluded or conditions change, and flagging edge cases for engineer review.

Requirements:

You have prior experience with CVAT, specifically with object tracking and interpolation workflows. You are comfortable working with video data across varied lighting and weather conditions. You have a high tolerance for repetitive, detail-oriented work and take quality seriously. Experience with YOLO-format datasets or prior computer vision annotation work is a strong plus.