3D & LiDAR Data Annotation Analyst

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Motivo actual: Puntuación 4: familia datos / BI / analytics (por título) · tu experiencia transferible presente: training / onboarding, compliance / standards · remoto.

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About the Role We’re building high-quality ground-truth datasets used to train and validate perception models for autonomous and operator-assisted machines working in complex, real-world environments. As a Data Annotation Analyst, you’ll work with camera imagery, video, and LiDAR point clouds to create precise labels that help perception systems understand objects, people, terrain, and surrounding environments. You’ll work in specialized annotation tools, following detailed specifications while balancing accuracy, consistency, and production volume. Your Impact Annotate 2D and 3D objects across images, video, and LiDAR point clouds, including vehicles, machinery, people, signage, and environmental features. Create and maintain accurate segmentation, object tracking, pose, and keypoint annotations across sequences. Apply detailed annotation guidelines, including class definitions, occlusion rules, object thresholds, and inclusion/exclusion criteria. Review your work, respond to QA feedback, and maintain established quality and accuracy standards. Identify and escalate ambiguous scenes, sensor artifacts, tooling issues, and gaps in specifications rather than making assumptions. Document data quality and tooling issues with clear details and reproduction steps. Participate in calibration sessions, guideline reviews, and team standups to maintain consistent interpretations. Share observations that help improve annotation guidelines, taxonomies, and edge-case documentation. Success in this role means producing accurate, consistent annotations at the expected volume while helping maintain a reliable, high-quality dataset. What You Bring Strong attention to detail and the ability to maintain accuracy while performing repetitive, visually intensive work for extended periods. Strong spatial reasoning skills, including the ability to interpret 3D environments and understand object size, position, and orientation. Ability to learn and navigate complex, purpose-built software and become productive with keyboard shortcuts and other tooling features. Experience working in a fast-paced, scaled environment with defined productivity, quality, or accuracy targets. Experience with image annotation GenAI workflows Reliable high-speed internet, a distraction-free workspace, and the ability to work on a company-provisioned machine within a controlled environment. Nice to Haves Associate or bachelor’s degree. Experience with image, video, LiDAR, or 3D annotation tools. Experience with subsurface utility engineering (SUE), construction surveying, surveying, GIS, or other work involving spatial interpretation of physical environments. Experience with AutoCAD or similar type tools Experience in construction, mining, agriculture, industrial operations, or heavy equipment environments. Familiarity with point cloud data, sensor fusion, or camera-LiDAR calibration. Experience working with autonomous vehicles, robotics, or machine perception datasets. Compensation Additional Information Why You’ll Love Working Here At Appen , we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest-quality AI solutions for frontier models. You’ll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You’ll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources and development opportunities to continue to build your capability over time. About Appen Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre-training, supervised fine tunin

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