Ebony Nixon

Key Skills

Software

Data Annotation Tech

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Top Label Types

RLHF

Evaluation Rating

Prompt Response Writing SFT

Bounding Box

Object Detection

Tracking

Freelancer Overview

I am a detail-oriented Computer Science graduate with hands-on experience in data annotation and AI training data. I have collaborated with leading AI developers to enhance Large Language Models (LLMs) through high-quality data labeling, RLHF, and fact-checking. My expertise spans semantic segmentation, named entity recognition (NER), sentiment analysis, and LiDAR labeling for computer vision and NLP projects. I am skilled in Python, Java, and SQL and have consistently delivered 98% accuracy across 5,000+ annotation tasks. My background in software development, technical troubleshooting, and quality assurance allows me to bridge the gap between raw data and machine learning excellence. I am passionate about optimizing data workflows and improving model performance through precise labeling and thoughtful prompt engineering.

Labeling Experience

Advanced RLHF & Technical Code Evaluation for LLM Refinement

Data Annotation TechComputer Code ProgrammingRLHFEvaluation Rating

I serve as a technical subject matter expert for training Large Language Models (LLMs) specifically in the domain of software development. My work involves interacting with experimental AI models to test their reasoning, logic, and ability to generate production-ready code.

I serve as a technical subject matter expert for training Large Language Models (LLMs) specifically in the domain of software development. My work involves interacting with experimental AI models to test their reasoning, logic, and ability to generate production-ready code.

2023 - 2025

High-Precision Computer Vision Training for Autonomous Systems

CVATImageBounding BoxObject Detection

This project involved the precise annotation of multi-sensor data to train perception models for autonomous driving and robotic navigation. Using a mix of 2D image frames and video sequences, I identified and classified environmental actors to improve the safety and reliability of machine learning algorithms.

This project involved the precise annotation of multi-sensor data to train perception models for autonomous driving and robotic navigation. Using a mix of 2D image frames and video sequences, I identified and classified environmental actors to improve the safety and reliability of machine learning algorithms.

2022 - 2023

Education

Work History