Location
Nairobi•Kenya
Contract Type
Location: Remote
About Fuzu Atlas
Fuzu Atlas is a leading organisation at the forefront of transforming the intersection between artificial intelligence and human activities. We collaborate with top-tier professionals and prominent industry players to push the boundaries of AI innovation and lead advancements in this dynamic field. By joining Fuzu, you will become an integral part of a company that prioritises leadership, quality, and professional growth.
Position Overview
We are seeking a highly detail-oriented and technically strongMulti-Sensor LiDAR Labeling Operations Policy & Quality Expertto support labeling policy development, quality management, and operational excellence across autonomous vehicle (AV) data annotation programs. The role involves working withLiDAR, camera, radar, and sensor-fusion annotation workflowsto define clear annotation standards, improve labeling quality, and support the development of high-quality datasets for autonomous driving systems.
Responsibilities:
Develop, maintain, and update annotation policies for 3D LiDAR object labeling, multi-sensor fusion, camera-LiDAR alignment, radar-assisted labeling, semantic segmentation, tracking, temporal consistency, and trajectory annotation.
Define taxonomy, ontology, edge-case handling, and escalation guidelines.
Translate perception model requirements into clear annotation specifications.
Create annotation playbooks, SOPs, decision trees, and reviewer guidelines.
Define quality metrics, acceptance criteria, and operational KPIs.
Design and support QA processes, including golden tasks, reviewer calibration, and inter-annotator agreement.
Conduct quality audits, identify recurring labeling issues, and drive corrective actions.
Analyze annotation productivity, ambiguity trends, and policy gaps.
Support workforce onboarding, certification, and calibration programs.
Collaborate with labeling vendors and BPO partners to implement annotation policies.
Work with perception, ML, tooling, and program teams to improve annotation quality and consistency.
Support dataset launches, quality reviews, and continuous improvement initiatives.
Minimum qualifications
Develop, maintain, and update annotation policies for 3D LiDAR object labeling, multi-sensor fusion, camera-LiDAR alignment, radar-assisted labeling, semantic segmentation, tracking, temporal consistency, and trajectory annotation.
Define taxonomy, ontology, edge-case handling, and escalation guidelines.
Translate perception model requirements into clear annotation specifications.
Create annotation playbooks, SOPs, decision trees, and reviewer guidelines.
Define quality metrics, acceptance criteria, and operational KPIs.
Design and support QA processes, including golden tasks, reviewer calibration, and inter-annotator agreement.
Conduct quality audits, identify recurring labeling issues, and drive corrective actions.
Analyze annotation productivity, ambiguity trends, and policy gaps.
Support workforce onboarding, certification, and calibration programs.
Collaborate with labeling vendors and BPO partners to implement annotation policies.
Work with perception, ML, tooling, and program teams to improve annotation quality and consistency.
Support dataset launches, quality reviews, and continuous improvement initiatives.
Requirements
Develop, maintain, and update annotation policies for 3D LiDAR object labeling, multi-sensor fusion, camera-LiDAR alignment, radar-assisted labeling, semantic segmentation, tracking, temporal consistency, and trajectory annotation.
Define taxonomy, ontology, edge-case handling, and escalation guidelines.
Translate perception model requirements into clear annotation specifications.
Create annotation playbooks, SOPs, decision trees, and reviewer guidelines.
Define quality metrics, acceptance criteria, and operational KPIs.
Design and support QA processes, including golden tasks, reviewer calibration, and inter-annotator agreement.
Conduct quality audits, identify recurring labeling issues, and drive corrective actions.
Analyze annotation productivity, ambiguity trends, and policy gaps.
Support workforce onboarding, certification, and calibration programs.
Collaborate with labeling vendors and BPO partners to implement annotation policies.
Work with perception, ML, tooling, and program teams to improve annotation quality and consistency.
Support dataset launches, quality reviews, and continuous improvement initiatives.
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