Ellon Timothy

Key Skills

Software

Labelbox

Appen

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Polygon

Segmentation

Classification

Tracking

Point Key Point

Emotion Recognition

Transcription

Freelancer Overview

I am an experienced AI data annotation specialist with over three years of hands-on work in creating high-quality training data for machine learning and computer vision projects. My expertise spans image, video, and audio annotation, including tasks like bounding box and polygon segmentation, semantic segmentation, keypoint annotation, multi-object tracking, and audio transcription with sentiment tagging and speaker diarization. I am highly proficient in industry-standard platforms such as Labelbox, Scale AI, CVAT, and VGG Image Annotator, and have supported projects in domains like autonomous driving, retail, and healthcare. I consistently maintain annotation accuracy above 98% and enjoy collaborating with ML engineers to refine guidelines and resolve edge cases. My background in computer information systems and certifications in machine learning and computer vision further strengthen my ability to deliver reliable, high-quality annotated datasets that drive model performance.

Labeling Experience

AI Data Annotation Specialist

AppenVideoBounding BoxPoint Key Point

Worked on large-scale video annotation projects involving multi-object tracking (MOT) across sequential frames using YOLO-based pipelines (YOLOv5, YOLOv8), DeepSORT, and ByteTrack. Responsibilities included assigning consistent object IDs across frames, performing temporal annotation, bounding box tracking, polygon segmentation, and instance segmentation on video datasets. Projects spanned industries including autonomous driving, retail, and action recognition. Collaborated with ML engineers to maintain annotation accuracy above 98%, ensuring high-quality training data for computer vision model development.

Worked on large-scale video annotation projects involving multi-object tracking (MOT) across sequential frames using YOLO-based pipelines (YOLOv5, YOLOv8), DeepSORT, and ByteTrack. Responsibilities included assigning consistent object IDs across frames, performing temporal annotation, bounding box tracking, polygon segmentation, and instance segmentation on video datasets. Projects spanned industries including autonomous driving, retail, and action recognition. Collaborated with ML engineers to maintain annotation accuracy above 98%, ensuring high-quality training data for computer vision model development.

2023

AI Data Annotation Specialist

LabelboxImageBounding BoxPolygon

Annotating large-scale image, video, and audio datasets for supervised machine learning models. Tasks include bounding box drawing, polygon and semantic segmentation, keypoint annotation, and multi-object tracking (MOT) using YOLO-based pipelines. Also transcribing and labeling audio files with speaker diarization, sentiment tags, and keyword spotting for NLP and voice AI applications. Consistently maintaining annotation accuracy above 98%

Annotating large-scale image, video, and audio datasets for supervised machine learning models. Tasks include bounding box drawing, polygon and semantic segmentation, keypoint annotation, and multi-object tracking (MOT) using YOLO-based pipelines. Also transcribing and labeling audio files with speaker diarization, sentiment tags, and keyword spotting for NLP and voice AI applications. Consistently maintaining annotation accuracy above 98%

2021 - 2022

AI Data Annotation Specialist

Scale AIAudioPolygonPoint Key Point

Worked on large-scale audio annotation projects involving transcription, speaker diarization, and sentiment tagging for NLP and voice AI model development. Responsibilities included segmenting audio files, labeling speakers across multi-speaker recordings, identifying keywords, and tagging emotional sentiment within audio clips. Used tools including Audacity and Praat to deliver accurate, high-quality labeled audio datasets. Maintained annotation accuracy above 98% through consistent QA reviews, supporting the development of speech recognition, voice assistant, and natural language processing AI systems.

Worked on large-scale audio annotation projects involving transcription, speaker diarization, and sentiment tagging for NLP and voice AI model development. Responsibilities included segmenting audio files, labeling speakers across multi-speaker recordings, identifying keywords, and tagging emotional sentiment within audio clips. Used tools including Audacity and Praat to deliver accurate, high-quality labeled audio datasets. Maintained annotation accuracy above 98% through consistent QA reviews, supporting the development of speech recognition, voice assistant, and natural language processing AI systems.

2021 - 2021

Education

Work History