US Driver's License Image Annotator (Bounding Boxes)

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow work that directly shapes how AI systems behave — with flexible remote projects, on-the-job experience, and the chance to work across many industries.

OpenTrain AI is hiring for this project. As a contributor you'll be part of a global community that prepares real examples used to train and improve machine learning models.

About AI Training Work

AI training (also called data labeling or annotation) is the human side of building modern AI. Annotators create and verify examples—like labeled images, transcriptions, or formatted records—that models learn from.

This kind of work is often fully remote, flexible, and accessible to people without specialized degrees. It rewards careful attention to detail and clear, consistent labeling.

The Role

You will review images of US driver's licenses and accurately annotate specific personal and document fields using bounding boxes in Label Studio.

This dataset will be used to train document-understanding and information-extraction models focused on US driver's licenses.

• Employment type: Contractor, Part-time
• Workload: Less than 20 hours per week
• Location: Remote — worldwide

What You’ll Do

Open and inspect images of US driver's licenses (crowdsourced templates) and place bounding boxes around requested elements, then enter or verify the field values as required by the labeling task.

• Label fields including: jurisdiction_name, credential_type, real_id_indicator, family_name, given_name, middle_name, suffix, date_of_birth, sex, height, weight, eye_color, hair_color, street_address, city, state, zip_code, document_number, class, endorsements, restrictions, date_of_issue, date_of_ex
• Apply bounding-box annotations precisely to the relevant areas of the image.
• Use Label Studio as the labeling interface and follow project-specific guidelines for consistency and accuracy.
• Flag low-quality or unreadable images per instructions.

Requirements

This is an entry-level annotation role. No formal education or prior experience is required, but accuracy and consistent labeling are essential.

• Basic familiarity with the appearance and common fields of US driver's licenses or state IDs.
• Ability to clearly read English text and recognize US address formats, dates, and document fields.
• Strong attention to detail and commitment to accurate extraction of information from images.
• Previous experience with data labeling, document annotation, or image tagging is a plus but not required.

Compensation & Tools

Pay is hourly (PAY_PER_HOUR). The posted hourly rate is $5 USD; project materials list an expected range of $1–$7 USD per hour. You will be contracted by OpenTrain AI and paid according to the platform's payment process.

Labeling will be done in Label Studio on image data using bounding-box label types.

• Data type: IMAGE
• Label types: BOUNDING_BOX
• Labeling software: LABEL_STUDIO

Who Should Apply & How It Works

This role is a good fit for detail-oriented people looking for flexible, part-time remote work, and for anyone interested in learning annotation skills used across AI projects.

To apply, create an OpenTrain account, complete any required onboarding or qualification task, and accept the contract. Work is performed as a contractor and scheduled by you within the project's time limits.

• Time commitment: Less than 20 hours/week; choose hours that fit your schedule.
• Worldwide applicants welcome — follow project instructions for eligibility and onboarding.