Madison Daniels

Key Skills

Software

Appen

Axiom AI

Clickworker

CloudFactory

CrowdSource

Data Annotation Tech

Hasty

OneForma

Remotasks

Toloka

Scale AI

Labelbox

V7 Labs

Top Subject Matter

Healthcare/Medical

Automotive/Transportation

Scientific Research

Top Data Types

Image

Text

Video

Top Task Types

Audio Recording

Classification

Data Collection

Object Detection

Prompt Response Writing SFT

Freelancer Overview

As a seasoned data labeling professional with over 3 years of specialized experience, I excel in training and evaluating large language models through careful annotation and systematic evaluation. My expertise spans text generation, sentiment analysis, and content moderation across multiple languages. I've successfully managed complex annotation projects for leading tech companies, consistently maintaining accuracy rates above 95%. Key strengths include developing detailed annotation guidelines, training junior annotators, and implementing quality control processes that improved team efficiency by 30%. I have hands-on experience with various annotation platforms including LabelBox and Scale AI, and I've worked extensively on projects involving chatbot response evaluation, content toxicity detection, and multi-turn dialogue assessment. My background in linguistics and natural language processing allows me to provide nuanced annotations that capture subtle semantic differences and contextual nuances.

Labeling Experience

Image Annotation for Autonomous Vehicle Perception

LabelboxImageBounding BoxPolygon

Annotated a large dataset of high-resolution images captured from various camera sensors mounted on self-driving vehicles. Labels included: Vehicles: Cars, trucks, motorcycles, bicycles, pedestrians. Traffic Objects: Traffic lights, stop signs, lane markings, crosswalks. Environmental Objects: Trees, buildings, road surfaces, obstacles. Segmentation: Precisely outlining the boundaries of vehicles and other relevant objects.

Annotated a large dataset of high-resolution images captured from various camera sensors mounted on self-driving vehicles. Labels included: Vehicles: Cars, trucks, motorcycles, bicycles, pedestrians. Traffic Objects: Traffic lights, stop signs, lane markings, crosswalks. Environmental Objects: Trees, buildings, road surfaces, obstacles. Segmentation: Precisely outlining the boundaries of vehicles and other relevant objects.

2023 - 2023

Medical Image Annotation for Disease Detection

V7 LabsMedical DicomBounding BoxSegmentation

Annotated a large dataset of medical images, including X-rays, CT scans, and MRIs, to assist in the development of computer-aided diagnosis (CAD) systems. Labels included: Disease Identification: Identifying the presence and location of various diseases, such as tumors, lesions, and fractures. Segmentation: Accurately outlining the boundaries of abnormal regions in the images. Classification: Categorizing images based on disease severity and other relevant factors.

Annotated a large dataset of medical images, including X-rays, CT scans, and MRIs, to assist in the development of computer-aided diagnosis (CAD) systems. Labels included: Disease Identification: Identifying the presence and location of various diseases, such as tumors, lesions, and fractures. Segmentation: Accurately outlining the boundaries of abnormal regions in the images. Classification: Categorizing images based on disease severity and other relevant factors.

2022 - 2022

Education

Work History