Website:
zapdoslabs.com
Job details:
Data Annotator Intern
Location: Remote
Type: Internship / Part-time / Full-time (flexible)
We’re an early-stage startup building an AI vision solution for real industrial environments, including factories, gyms, and construction sites.
We’re looking for a detail-oriented Data Annotation Intern to help prepare and validate the image and video datasets used to train our computer vision models.
What you'll do
- Annotate images and videos using bounding boxes, segmentation masks, classifications, and tracking labels
- Review model predictions and correct inaccurate annotations
- Identify unusual cases, poor-quality footage, and unclear labeling scenarios
- Follow and improve annotation guidelines
- Perform quality checks across labeled datasets
- Organize datasets and track annotation progress
- Communicate recurring issues and edge cases to the engineering team
- Handle customer footage carefully and confidentially
What we're looking for
- Strong attention to detail and consistency
- Comfortable performing focused, repetitive work
- Reliable, organized, and able to meet agreed deadlines
- Able to follow detailed annotation instructions
- Comfortable working with images, videos, and browser-based tools
- Able to communicate unclear cases practically
Nice to have
- Experience with CVAT, Label Studio, Roboflow, or similar tools
- Interest in computer vision or applied AI
- Familiarity with industrial, construction, fitness, or safety environments
- Basic understanding of object detection, segmentation, or video tracking
- Experience reviewing or cleaning datasets
Compensation & stage
We are a young, early-stage startup currently converting our first enterprise leads.
Compensation includes:
- Cash allowance
- Equity upside reflecting early contribution and ownership
We’re transparent about our stage and constraints. We’re looking for someone excited to contribute directly to real-world AI systems and grow with the company.
Why this role
Your work will directly improve models deployed in real industrial environments—not just demos or research projects. You’ll learn how production computer vision datasets are created, validated, and used to build digital twins from real-world video.
Click on Apply to know more.