Samantha Spillers

Key Skills

Software

Labelbox

SuperAnnotate

CVAT

Top Subject Matter

Computer Vision

Natural Language Processing

Speech Recognition

Top Data Types

Image

Text

Audio

Document

Top Task Types

Bounding Box

Entity (NER) Classification

Transcription

Classification

Freelancer Overview

Data Labeling Specialist (Freelance / Remote). Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Labelbox, SuperAnnotate, and CVAT. Education includes High School Diploma, Northeast High School (2011). AI-training focus includes data types such as Image, Text, and Audio and labeling workflows including Bounding Box, Entity (NER) Classification, and Transcription.

Labeling Experience

Audio Annotation & Transcription Specialist

SuperannotateAudioTranscription

I transcribed and labeled over 5,000 audio clips to support speech recognition and voice AI training. My responsibilities included annotating features such as tone, pauses, and speaker identification to enhance speech model accuracy. I achieved high accuracy rates by thoroughly reviewing and validating all transcribed datasets. • Tagged audio data elements relevant for speech-to-text applications. • Maintained quality and reliability standards across all audio annotation tasks. • Utilized annotation tools capable of handling audio formats (e.g., Labelbox, SuperAnnotate). • Collaborated with team members to address and resolve ambiguous cases.

I transcribed and labeled over 5,000 audio clips to support speech recognition and voice AI training. My responsibilities included annotating features such as tone, pauses, and speaker identification to enhance speech model accuracy. I achieved high accuracy rates by thoroughly reviewing and validating all transcribed datasets. • Tagged audio data elements relevant for speech-to-text applications. • Maintained quality and reliability standards across all audio annotation tasks. • Utilized annotation tools capable of handling audio formats (e.g., Labelbox, SuperAnnotate). • Collaborated with team members to address and resolve ambiguous cases.

2023 - Present

NLP Annotation Specialist

LabelboxTextEntity Ner Classification

As part of my NLP annotation work, I labeled and categorized text datasets for sentiment analysis, intent classification, and named entity recognition. Tasks included tagging for people, locations, organizations, and categorizing customer feedback for chatbot model training. I maintained high accuracy and consistency while following detailed labeling guidelines. • Performed sentiment classification (positive, negative, neutral) and NER tagging. • Supported data prep for AI conversational and classification tasks. • Used industry-standard annotation tools for efficient task management. • Ensured labeling consistency with strict adherence to project instructions.

As part of my NLP annotation work, I labeled and categorized text datasets for sentiment analysis, intent classification, and named entity recognition. Tasks included tagging for people, locations, organizations, and categorizing customer feedback for chatbot model training. I maintained high accuracy and consistency while following detailed labeling guidelines. • Performed sentiment classification (positive, negative, neutral) and NER tagging. • Supported data prep for AI conversational and classification tasks. • Used industry-standard annotation tools for efficient task management. • Ensured labeling consistency with strict adherence to project instructions.

2023 - Present

Data Labeling Specialist (Freelance / Remote)

LabelboxImageBounding Box

As a Data Labeling Specialist, I annotated and labeled over 50,000 data points across multiple projects for AI model training. My focus included applying bounding boxes, semantic segmentation, and classification to image datasets for computer vision and ensuring over 98% annotation accuracy. I used industry-standard tools and stringent quality checks to maintain high-quality outputs. • Applied object detection and image classification techniques for datasets involving vehicles, pedestrians, and various items. • Utilized Labelbox, CVAT, and SuperAnnotate for annotation tasks and data management. • Collaborated remotely with QA teams to resolve edge cases and improve consistency. • Consistently met 100% of project deadlines while managing multiple concurrent labeling assignments.

As a Data Labeling Specialist, I annotated and labeled over 50,000 data points across multiple projects for AI model training. My focus included applying bounding boxes, semantic segmentation, and classification to image datasets for computer vision and ensuring over 98% annotation accuracy. I used industry-standard tools and stringent quality checks to maintain high-quality outputs. • Applied object detection and image classification techniques for datasets involving vehicles, pedestrians, and various items. • Utilized Labelbox, CVAT, and SuperAnnotate for annotation tasks and data management. • Collaborated remotely with QA teams to resolve edge cases and improve consistency. • Consistently met 100% of project deadlines while managing multiple concurrent labeling assignments.

2023 - Present

Computer Vision Image Annotation Project

CVATImageBounding Box

In my Computer Vision Image Annotation Project, I annotated over 20,000 images using bounding boxes and polygon segmentation. I was responsible for identifying and labeling objects such as vehicles and pedestrians while consistently following detailed labeling guidelines. My contributions supported improvements to object detection model accuracy. • Applied bounding boxes and polygon annotations to images for object detection. • Ensured high annotation consistency through guideline adherence. • Used robust annotation tools to manage large datasets. • Collaborated in a team setting to enhance labeling reliability.

In my Computer Vision Image Annotation Project, I annotated over 20,000 images using bounding boxes and polygon segmentation. I was responsible for identifying and labeling objects such as vehicles and pedestrians while consistently following detailed labeling guidelines. My contributions supported improvements to object detection model accuracy. • Applied bounding boxes and polygon annotations to images for object detection. • Ensured high annotation consistency through guideline adherence. • Used robust annotation tools to manage large datasets. • Collaborated in a team setting to enhance labeling reliability.

2024 - 2024

Natural Language Processing (NLP) Annotation Project

LabelboxTextEntity Ner Classification

In the NLP Annotation Project, I labeled training data for sentiment classification and performed NER tagging for people, locations, and organizations. This work contributed to the quality of AI conversational models and chatbots by categorizing feedback and maintaining labeling consistency, especially in ambiguous cases. I followed project instructions and stringent quality control checks throughout the task. • Conducted sentiment analysis and entity recognition on text datasets. • Categorized and prepared training data for conversational AI apps. • Maintained high accuracy in NLP data labeling across challenging samples. • Utilized industry annotation tools and collaborated with other annotators.

In the NLP Annotation Project, I labeled training data for sentiment classification and performed NER tagging for people, locations, and organizations. This work contributed to the quality of AI conversational models and chatbots by categorizing feedback and maintaining labeling consistency, especially in ambiguous cases. I followed project instructions and stringent quality control checks throughout the task. • Conducted sentiment analysis and entity recognition on text datasets. • Categorized and prepared training data for conversational AI apps. • Maintained high accuracy in NLP data labeling across challenging samples. • Utilized industry annotation tools and collaborated with other annotators.

2023 - 2024

Show more

Education

Work History