Key Skills

Software

Clickworker

CVAT

Dataloop

Labelbox

Mindrift

OneForma

Remotasks

Scale AI

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Image

Video

Top Label Types

Bounding Box

Cuboid

Polygon

Polyline

Segmentation

Freelancer Overview

I am a highly skilled Data Annotation Specialist with over 4 years of experience contributing to AI and machine learning projects across diverse domains, including autonomous vehicles, natural language processing, and content moderation. My expertise spans across annotating 2D and 3D datasets such as LiDAR, RGB, and radar point cloud data as well as handling text, audio, and image labeling tasks with precision and consistency. I’ve worked on leading platforms such as Remotasks, Scale AI, and Appen, and I'm proficient with industry-standard tools like Labelbox, CVAT, SuperAnnotate, and Dataloop. In addition to hands-on labeling, I bring strong quality assurance and feedback integration skills, ensuring that data meets strict accuracy standards. I’m actively engaged in the LLM Pre-Qualification Community, sharpening my skills in model evaluation and AI workflows. With a Bachelor’s degree in Computer Science, a fast learning curve, and experience collaborating with global remote teams, I’m confident in delivering high-quality AI training data that fuels real-world innovation.

Labeling Experience

Data Annotator

Scale AIVideoBounding BoxPolygon

The project involved large-scale annotation of multimodal datasets to support AI training for autonomous driving, natural language processing, and computer vision applications. I performed a range of labeling tasks, including 3D point cloud annotation (LiDAR, Radar, RGB), image segmentation using bounding boxes and polygons, text classification, sentiment analysis, and audio transcription. The dataset sizes ranged from thousands to hundreds of thousands of data points, depending on the task. I followed detailed guidelines and participated in regular calibration checks to ensure my work met accuracy benchmarks (typically 95%+). Quality control processes included peer reviews, spot checks by QA teams, and feedback integration to maintain consistency and data reliability. I consistently ranked among the top contributors in terms of both speed and precision, demonstrating a strong ability to adapt to evolving project requirements and maintain high annotation standards.

The project involved large-scale annotation of multimodal datasets to support AI training for autonomous driving, natural language processing, and computer vision applications. I performed a range of labeling tasks, including 3D point cloud annotation (LiDAR, Radar, RGB), image segmentation using bounding boxes and polygons, text classification, sentiment analysis, and audio transcription. The dataset sizes ranged from thousands to hundreds of thousands of data points, depending on the task. I followed detailed guidelines and participated in regular calibration checks to ensure my work met accuracy benchmarks (typically 95%+). Quality control processes included peer reviews, spot checks by QA teams, and feedback integration to maintain consistency and data reliability. I consistently ranked among the top contributors in terms of both speed and precision, demonstrating a strong ability to adapt to evolving project requirements and maintain high annotation standards.

2020 - 2024

Education

Work History