Jjadasa33

Key Skills

Software

Labelbox

Appen

CVAT

Scale AI

AWS SageMaker

Axiom AI

Anno-Mage

Argilla

Top Subject Matter

E-commerce and Advertising

Sentiment Analysis and Analytics

Top Data Types

Image

Text

Audio

Top Task Types

Classification

Entity Ner Classification

Text Generation

Segmentation

RLHF

Fine Tuning

Red Teaming

Text Summarization

Computer Programming Coding

Data Collection

Function Calling

Transcription

Bounding Box

Object Detection

Prompt Response Writing SFT

Evaluation Rating

Question Answering

Cuboid

Point Key Point

Polygon

Freelancer Overview

Senior Machine Learning Engineer (Data Labeling, TechTonic Ltd). Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox. Education includes Master of Science, University of Edinburgh (2014) and Bachelor of Science, University of Manchester (2013). AI-training focus includes data types such as Image and Text and labeling workflows including Classification and Entity (NER) Classification.

Labeling Experience

Senior Machine Learning Engineer (Data Labeling, TechTonic Ltd)

LabelboxImageClassification

Responsible for building datasets used in training deep learning models for image recognition and recommendation systems. Developed, annotated, and curated large volumes of image data to support e-commerce and ad targeting ML pipelines. Collaborated with team to optimize manual annotation processes for accuracy and quality. • Used distributed pipelines to handle and process over 100TB of image data. • Leveraged Labelbox and CVAT for data annotation and quality checks. • Built real-time pipelines to improve model inference using labeled datasets. • Ensured consistent data labeling practices aligned with business requirements.

Responsible for building datasets used in training deep learning models for image recognition and recommendation systems. Developed, annotated, and curated large volumes of image data to support e-commerce and ad targeting ML pipelines. Collaborated with team to optimize manual annotation processes for accuracy and quality. • Used distributed pipelines to handle and process over 100TB of image data. • Leveraged Labelbox and CVAT for data annotation and quality checks. • Built real-time pipelines to improve model inference using labeled datasets. • Ensured consistent data labeling practices aligned with business requirements.

2020 - Present

Machine Learning Engineer (NLP Data Annotation, DataQuantum)

LabelboxTextEntity Ner Classification

Built and annotated large NLP datasets for sentiment analysis models deployed at scale. Performed entity classification and sentiment scoring to enable analytics pipelines servicing 500K+ users. Streamlined annotation processes to reduce manual workload and support rapid new data ingestion. • Coordinated labeling of thousands of text samples for supervised learning. • Integrated annotation workflows with internal and public text datasets. • Used Labelbox for annotation management and progress tracking. • Ensured high quality and balanced class distributions in labeled datasets.

Built and annotated large NLP datasets for sentiment analysis models deployed at scale. Performed entity classification and sentiment scoring to enable analytics pipelines servicing 500K+ users. Streamlined annotation processes to reduce manual workload and support rapid new data ingestion. • Coordinated labeling of thousands of text samples for supervised learning. • Integrated annotation workflows with internal and public text datasets. • Used Labelbox for annotation management and progress tracking. • Ensured high quality and balanced class distributions in labeled datasets.

2017 - 2019

Education

Work History