Background

DDD believes talent has no boundaries--and opportunities shouldn’t either. In 2001, we saw the need to bring tech skills and living-wage work to men and women in underserved communities in Asia. It was here that DDD helped plant the seed for a socially responsible outsourcing practice known as impact sourcing.

Job Description

Are you an experienced AV or ADAS data labeler in Kenya? Digital Divide Data (DDD) is hosting a 2-week Boot Camp for data annotation professionals who excel in quality, accuracy, consistency, and detail-oriented Autonomous Vehicle operations.

Applicants should have practical experience in one or more of the following:

• LiDAR and point-cloud annotation.
• 2D and 3D bounding boxes.
• Image and video annotation.
• Object detection, classification, and tracking.
• Polygon, semantic, or instance segmentation.
• Lane, road, pedestrian, vehicle, and environmental feature annotation.
• Autonomous Vehicle or ADAS quality assurance.
• Interpretation and application of detailed annotation guidelines.
• Language-based tasks like transcription, captioning, and prompt-response writing

Qualifications

• Have 1+ years of hands-on experience in Autonomous Vehicle, ADAS, or closely related data annotation work.
• Demonstrate a strong understanding of annotation quality standards and guidelines.
• Have excellent attention to detail and the ability to work accurately on repetitive and complex tasks.
• Be able to meet defined productivity and quality expectations.
• Be available for potential project deployment after successfully completing the assessment process.
• Not currently enrolled as a student.
• Be willing to complete experience verification and a practical skills assessment.
• Reading and writing proficiency in English

How will the Training programme work?

The Training will be a 2-week, 80-hour in-person boot camp combining theory, practical exercises, and daily skill checks.

• AV fundamentals: Scene understanding, object classification, LiDAR/point clouds, 3D annotation, precision, and 2D/3D correlation.
• Driving and temporal reasoning: Tracking over time, ego behaviour, traffic and road context, agent interaction, and spatio-temporal reasoning.
• Advanced reasoning: Logical linking, causal/VLA reasoning, and working with evidence and uncertainty.
• Quality and adaptability: Attention to detail, learning agility, adapting to changing guidelines, and independent QA.
• Hands-on application: Learners apply concepts through practical annotation tasks, calibration, feedback, and exercises, not classroom learning alone.
• Post-Training assessment: Five evaluation tasks combining tool-based scoring and expert review, with critical gates for technical execution, reasoning, quality, and adaptability.
• Production readiness: Trainees who meet the required standard move into production as project opportunities become available

Trainees must complete the full programme and meet defined quality and proficiency standards to successfully complete it. Successful completion does not guarantee immediate employment. Qualified participants will join DDD’s pre-screened talent bench and may be considered for future project assignments based on client demand and individual availability.

Note: This will be an in-person boot camp held at the DDD Nairobi offices. A training stipend will be reimbursed to all the successful trainees upon completion of the 2-week boot camp

• LiDAR and point-cloud annotation
• 2D and 3D bounding boxes
• Image and video annotation
• Object detection, classification, and tracking
• Polygon, semantic, or instance segmentation
• Lane, road, pedestrian, vehicle, and environmental feature annotation
• Autonomous Vehicle or ADAS quality assurance
• Interpretation and application of detailed annotation guidelines
• Transcription
• Captioning
• Prompt-response writing
• Attention to detail
• Accuracy
• Consistency
• Detail-oriented work
• English proficiency

• 1+ years of hands-on experience in Autonomous Vehicle, ADAS, or closely related data annotation work.
• Strong understanding of annotation quality standards and guidelines.
• Excellent attention to detail and ability to work accurately on repetitive and complex tasks.
• Ability to meet defined productivity and quality expectations.
• Availability for potential project deployment after successfully completing the assessment process.
• Not currently enrolled as a student.
• Willingness to complete experience verification and a practical skills assessment.
• Reading and writing proficiency in English.