Joseph G.

Key Skills

Software

Labelbox

Appen

Top Subject Matter

Nlp Domain Expertise

Rlhf Domain Expertise

LLM fine-tuning

Top Data Types

Text

Image

Document

Top Task Types

Entity (NER) Classification

Classification

Freelancer Overview

Senior Freelance Data Annotator. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Appen, and N. Education includes Master of Science, University of Maryland (2022). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification and Classification.

Labeling Experience

Senior Freelance Data Annotator

LabelboxTextEntity (NER) Classification

This experience involved leading annotation projects for large-scale NLP datasets using advanced techniques. I supervised a team to calibrate annotations, maintained high agreement metrics, and annotated complex instruction-following datasets for RLHF pipelines. The work included developing guidelines, delivering thousands of samples, and conducting thorough QA audits. • Labeled multilingual corpora for NER, coreference resolution, and relation extraction. • Annotated and validated instruction-following datasets for LLM fine-tuning. • Utilized Labelbox and Scale AI under quality-controlled processes. • Ensured all schemas and requirements were continually met.

2024 - 2026

Freelance Data Annotator

AppenTextClassification

This role covered annotating diverse text, image, and audio datasets for various AI model types such as computer vision, ASR, and sentiment analysis. I performed quality-controlled labeling including bounding boxes, polygon segmentation, classification, and transcription for high annotation accuracy. Key responsibilities included transcribing audio, labeling for sentiment, and applying sophisticated annotation types. • Used bounding boxes, landmark detection, and segmentation for object recognition. • Labeled and transcribed audio for speech recognition and diarization models. • Achieved annotation accuracy rates above 98%. • Utilized Appen, Lionbridge, CVAT, and Toloka for high-volume tasks.

2022 - 2023

Data Science Intern – Data Labeling Tasks

TextClassification

In this internship, I participated in the cleaning and labeling of training data for early-stage machine learning model development. My main tasks included labeling text and images for model experiments and documenting the annotation process. The experience provided exposure to efficient data workflows in a startup setting. • Labeled text data for classification applications. • Annotated images for recognition model testing. • Assisted with onboarding and maintaining process documentation. • Supported development of clean datasets for ML training.

2022 - 2022

Education

Work History