Timothy Kipkogei Kibii

Labeled sensor and multimodal data, including LiDAR point clouds, camera feeds, and robotic arm trajectories, to support robotics and autonomous systems training. Annotated object detection, spatial relationships, and motion sequences for robotics perception and manipulation datasets. Followed technical guidelines to ensure precise, consistent labeling across complex 3D and multi-sensor data, and partnered with QA reviewers to validate accuracy and troubleshoot ambiguous scenarios.

Annotated and labeled large volumes of video footage with frame-accurate bounding boxes, object tracking, and event tagging to support AI training datasets. Applied detailed annotation guidelines to classify actions, objects, and scene context across diverse video content types. Conducted quality review passes to ensure consistency, accuracy, and alignment with project specifications, and collaborated with remote teams to resolve edge cases and refine labeling protocols.

Assessed AI-generated text, audio, and video content through comparative response ranking, reasoning evaluation, and prompt analysis. Produced structured written feedback used to improve large language model accuracy and reasoning in RLHF-style workflows. Performed data labeling, prompt engineering and rewriting, annotation, and dataset validation while strictly following guidelines and maintaining high quality thresholds; documented findings and edge cases for project leads and QA teams.