Additionalinformation

Additional Information

Selection Process

The selection process is expected to include:

1. Application review: Review applications and previous AV labeling experience to establish the candidate’s technical baseline.

2. Initial screening and interview: Assess relevant experience, availability, reliability, and suitability for the programme.

3. Practical annotation assessment: Candidates complete a practical assessment to demonstrate their existing annotation skills before selection for training.

4. Training boot camp: Selected candidates complete the intensive 2-week programme combining technical instruction, practical exercises, calibration, and daily skill checks.

5. Final readiness assessment: Evaluate quality, technical proficiency, reasoning, adaptability, and overall readiness for production.

6. Pre-qualified talent bench: Candidates who successfully complete the programme are placed on a ready bench for consideration against future AV project requirements.

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.

1. AV fundamentals: Scene understanding, object classification, LiDAR/point clouds, 3D annotation, precision, and 2D/3D correlation.

2. Driving and temporal reasoning: Tracking over time, ego behaviour, traffic and road context, agent interaction, and spatio-temporal reasoning.

3. Advanced reasoning: Logical linking, causal/VLA reasoning, and working with evidence and uncertainty.

4. Quality and adaptability: Attention to detail, learning agility, adapting to changing guidelines, and independent QA.

5. Hands-on application: Learners apply concepts through practical annotation tasks, calibration, feedback, and exercises, not classroom learning alone.

6. Post-Training assessment: Five evaluation tasks combining tool-based scoring and expert review, with critical gates for technical execution, reasoning, quality, and adaptability.

7. 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

We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, or disability status. If interested kindly submit your application on or before 9th October 2026

Companydescription

Company Description

About Digital Divide Data (DDD)

Digital Divide Data (DDD) is a BPO that delivers ML data solutions and content services to Fortune 500 companies and the world’s leading academic institutions. DDD is unique in its ability to deliver end-to-end data creation, curation, labeling, and annotation services, regardless of scale, with a guaranteed level of quality.

Jobdescription

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:

1. LiDAR and point-cloud annotation.

2. 2D and 3D bounding boxes.

3. Image and video annotation.

4. Object detection, classification, and tracking.

5. Polygon, semantic, or instance segmentation.

6. Lane, road, pedestrian, vehicle, and environmental feature annotation.

7. Autonomous Vehicle or ADAS quality assurance.

8. Interpretation and application of detailed annotation guidelines.

9. Language-based tasks like transcription, captioning, and prompt-response writing

Qualifications

1. Have 1+ years of hands-on experience in Autonomous Vehicle, ADAS, or closely related data annotation work.

2. Demonstrate a strong understanding of annotation quality standards and guidelines.

3. Have excellent attention to detail and the ability to work accurately on repetitive and complex tasks.

4. Be able to meet defined productivity and quality expectations.

5. Be available for potential project deployment after successfully completing the assessment process.

6. Not currently enrolled as a student.

7. Be willing to complete experience verification and a practical skills assessment.

8. Reading and writing proficiency in English