From the employer's hiring system · Verified employer
Data Labelers- AV/ADAS
digital divide data
Job at a glance
- Employer
- digital divide data
- Position
- Data Labelers- AV/ADAS
- Location
- Nairobi, Nairobi County, Kenya
- Possible duty stations
- Nairobi
- Job type
- Contract
- Contract
- 2-week training boot camp
- Experience
- 1+ years in AV/ADAS data annotation
- Salary
- Not stated in the advert
- Application deadline
- Open until filled (listed until 17 October 2026)
- Posted on VacancyKE
- 29 September 2026
VacancyKE summary
Digital Divide Data is recruiting experienced AV/ADAS data labelers in Nairobi for a 2-week in-person boot camp. The programme prepares participants for potential future project assignments on DDD's pre-screened talent bench.
What stands out about this opportunity
- In-person boot camp in Nairobi
- Reimbursable training stipend provided
- Deadline: 9th October 2026
- No guaranteed immediate employment
What’s offered
- Training stipend reimbursed upon successful completion of the 2-week boot camp
- Placement on a pre-screened talent bench for consideration against future projects
Key responsibilities
- Complete an intensive 2-week, 80-hour in-person training boot camp
- Learn AV fundamentals including LiDAR, point clouds, and 3D annotation
- Apply advanced reasoning skills to tracking, ego behaviour, and traffic context
- Perform hands-on practical annotation tasks with daily skill checks
- Undergo post-training assessments involving tool-based scoring and expert review
Who should apply?
Experienced data annotators specializing in autonomous vehicles who are available for in-person training in Nairobi and seeking placement on a professional talent bench.
Key requirements
- 1+ years AV/ADAS annotation experience
- LiDAR and 3D bounding box skills
- Not currently a student
- English reading/writing proficiency
- Attention to detail and accuracy
- Available for in-person training
Nice to have
- Experience in language-based tasks like transcription, captioning, or prompt-response writing
- Background in object detection, classification, tracking, and environmental feature annotation
What you should know
- Boot camp is held in person at Digital Divide Data’s Nairobi offices
- Successful completion does not guarantee immediate employment
- Future assignments depend on client demand and individual availability
- Selection involves application review, interview, and practical annotation assessment
How to apply
- Submit your application on or before 9th October 2026
Official source: From the employer's hiring system · jobs.smartrecruiters.com. VacancyKE never charges job seekers.
Original employer advertisement
Published by digital divide data on jobs.smartrecruiters.com. Shown for reference; the employer’s version is the official one.
The employer’s advert
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
• 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
Selection Process
The selection process is expected to include:
• Application review: Review applications and previous AV labeling experience to establish the candidate’s technical baseline.
• Initial screening and interview: Assess relevant experience, availability, reliability, and suitability for the programme.
• Practical annotation assessment: Candidates complete a practical assessment to demonstrate their existing annotation skills before selection for training.
• Training boot camp: Selected candidates complete the intensive 2-week programme combining technical instruction, practical exercises, calibration, and daily skill checks.
• Final readiness assessment: Evaluate quality, technical proficiency, reasoning, adaptability, and overall readiness for production.
• 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.
• 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
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
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
• 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
Selection Process
The selection process is expected to include:
• Application review: Review applications and previous AV labeling experience to establish the candidate’s technical baseline.
• Initial screening and interview: Assess relevant experience, availability, reliability, and suitability for the programme.
• Practical annotation assessment: Candidates complete a practical assessment to demonstrate their existing annotation skills before selection for training.
• Training boot camp: Selected candidates complete the intensive 2-week programme combining technical instruction, practical exercises, calibration, and daily skill checks.
• Final readiness assessment: Evaluate quality, technical proficiency, reasoning, adaptability, and overall readiness for production.
• 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.
• 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
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