Russell Kenedy

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Freelancer Overview

I am a detail-oriented Computer Science graduate with 1–3 years of hands-on experience in AI data labeling, annotation, and large language model (LLM) evaluation. My background includes annotating and categorizing conversational AI datasets, classifying outputs for relevance, safety, and factual accuracy, and ranking model responses to support reinforcement learning from human feedback (RLHF) workflows. I am skilled at detecting hallucinations, verifying information, and applying structured annotation guidelines to maintain high-quality, consistent training data. My expertise spans prompt classification, instruction-following evaluation, and writing clear, structured justifications for annotation decisions. I am proficient in Python for basic scripting, Google Docs, Sheets, Markdown, and have experience with text classification and dataset categorization—primarily in the natural language processing (NLP) domain. My strong analytical, research, and written communication skills enable me to contribute effectively to data quality assurance and AI model improvement initiatives.

Labeling Experience

High-Precision Multimodal Data Annotation for Computer Vision Model

LabelboxImageBounding BoxPoint Key Point

Performed high-precision image data annotation for multimodal and computer vision machine learning models using Labelbox. Annotated large image datasets by creating bounding boxes, polygon segmentation masks, keypoints, object classification labels, and object tracking across image sequences. Labeled real-world objects such as vehicles, pedestrians, road signs, and environmental features for autonomous perception systems. Followed detailed annotation guidelines and performed quality assurance checks to maintain dataset consistency and accuracy. Reviewed edge cases, corrected labeling errors, and validated annotations before submission. Ensured pixel-level accuracy for segmentation and spatial precision for bounding boxes while maintaining consistent labeling across datasets. The annotated data was prepared for training and evaluation of computer vision and multimodal AI models used for object detection, recognition, and scene understanding tasks.

Performed high-precision image data annotation for multimodal and computer vision machine learning models using Labelbox. Annotated large image datasets by creating bounding boxes, polygon segmentation masks, keypoints, object classification labels, and object tracking across image sequences. Labeled real-world objects such as vehicles, pedestrians, road signs, and environmental features for autonomous perception systems. Followed detailed annotation guidelines and performed quality assurance checks to maintain dataset consistency and accuracy. Reviewed edge cases, corrected labeling errors, and validated annotations before submission. Ensured pixel-level accuracy for segmentation and spatial precision for bounding boxes while maintaining consistent labeling across datasets. The annotated data was prepared for training and evaluation of computer vision and multimodal AI models used for object detection, recognition, and scene understanding tasks.

2021 - 2025

Education

Work History