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About the role
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 strong Multi-Sensor LiDAR Labeling Operations Policy and Quality Expert to support labeling policy development, quality management, and operational excellence across autonomous vehicle (AV) data annotation programs. The role involves working with LiDAR, camera, radar, and sensor-fusion annotation workflows to define clear annotation standards, improve labeling quality, and support the development of high-quality datasets for autonomous driving systems. Key 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. Key Requirements Bachelor’s degree in Engineering, Computer Science, Robotics, Data Science, GIS, or a related field. 5+ years of experience in autonomous vehicle data annotation, LiDAR labeling operations, quality assurance, or annotation policy development. Strong understanding of 3D point cloud data, sensor fusion systems, object tracking, trajectory labeling, and AV perception pipelines. Experience working with annotation tools supporting LiDAR, multi-camera systems, radar, or HD maps. Proven experience managing annotation quality at scale. Strong analytical and problem-solving skills. Excellent documentation and stakeholder communication skills. Ability to work effectively with technical, operational, and vendor teams. A must have to be considered: A working personal laptop (Please note this will be verified while shortlisting) An internet connection of 15 MBPS and above Applications will be reviewed on a rolling basis therefore the sooner you apply the better.
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