Multi-Sensor LiDAR Labeling Operations & Quality Expert

About the role

Fuzu Atlas is seeking a highly detail‑oriented expert to lead policy development, quality management, and operational excellence for multi‑sensor LiDAR labeling programs supporting autonomous vehicle perception systems. The role works remotely with cross‑functional teams to define annotation standards, improve data quality, and ensure consistent, high‑quality datasets for AI‑driven driving solutions.

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 specifications.
• Create playbooks, SOPs, decision trees, and reviewer guidelines; set quality metrics, acceptance criteria, and operational KPIs.
• Design QA processes including golden tasks, reviewer calibration, and inter‑annotator agreement; conduct audits, identify recurring issues, and drive corrective actions.
• Analyze productivity, ambiguity trends, and policy gaps; support onboarding, certification, and calibration programs for annotators.
• Collaborate with labeling vendors, BPO partners, and internal perception, ML, tooling, and program teams to continuously improve annotation quality.

Required profile

• Bachelor’s degree in Engineering, Computer Science, Robotics, Data Science, GIS, or related field.
• 5+ years of experience in autonomous‑vehicle data annotation, LiDAR labeling operations, quality assurance, or annotation policy development.
• Strong analytical and problem‑solving abilities with excellent documentation and stakeholder communication skills.

Required skills

• 3D point‑cloud data handling
• Sensor fusion systems
• Object tracking and trajectory labeling
• AV perception pipelines
• LiDAR, multi‑camera, radar, and HD‑map annotation tools