Information TechnologyFull-TimeJunior-level(1-2 yrs)
Job Description
Role Overview
The Technical Operator Level 1 is responsible for executing 2D and 3D LiDAR annotation and segmentation tasks in accordance with defined Standard Operating Procedures (SOPs), quality benchmarks, and productivity targets. This role requires technical precision, spatial awareness, and disciplined execution in high-volume production environments.
Responsibilities
Production & Quality Execution
Execute repetitive 2D/3D LiDAR annotation and segmentation tasks in strict adherence to SOPs.
Maintain classification accuracy across object types and categories.
Meet or exceed defined benchmarks for productivity, quality, and accuracy.
Sustain consistency in output with minimal supervision.
Issue Identification & Continuous Improvement
Identify recurring annotation errors or tool-related issues.
Escalate quality risks or inconsistencies in labeling standards.
Suggest improvements to tools, taxonomy, or workflow.
Communication & Collaboration
Communicate effectively with peers, QA teams, and stakeholders in English.
Document issues clearly and accurately.
Success Profile
High attention to detail.
Strong spatial and logical reasoning ability.
Ability to sustain accuracy in repetitive workflows.
Foundational understanding of quality control.
Ability to identify misclassification and segmentation inconsistencies.
Qualifications
Education Requirements
Diploma or higher qualification in a relevant field such as:
Computer Science
Information Technology
Engineering (Electrical, Computer, Geospatial, or related)
Data Science
Geospatial Studies
Or equivalent technical discipline
Technical Competencies
Working knowledge of 2D LiDAR annotation.
Working knowledge of 3D point cloud annotation.
Proficient working knowledge of a computer/laptop.
Strong English reading comprehension and the ability to write clear and accurate English.
Ability to interpret and execute complex SOP documentation.
Ability to perform basic object segmentation and classification.
Understanding of bounding boxes, cuboids, and object tagging principles.
Ability to follow annotation taxonomies and ontology guidelines accurately.
Additional Information
Familiarity with annotation tools such as CVAT, SuperAnnotate, and Labelbox.
Understanding of ML metrics, data quality principles, and AV/ADAS ecosystems.
How to Apply
Interested and qualified candidates should apply online via the following application link: https://www.myjobmag.co.ke/apply-now/1166128. This link will redirect you to the Digital Divide Data application portal on smrtr.io.