Key Skills

Software

Labelbox

Label Studio

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Label Types

Audio Recording

Classification

Computer Programming Coding

Data Collection

Evaluation Rating

Fine Tuning

Prompt Response Writing SFT

Question Answering

RLHF

Transcription

Freelancer Overview

I have extensive experience working with AI training data, focusing on data labeling, quality assurance, and dataset evaluation for machine learning systems. My work involves reviewing labeled datasets, verifying the accuracy of annotations, extracting relevant information from images, and correcting inconsistencies to improve dataset quality. I am highly detail-oriented and skilled at analyzing visual content, ensuring that answers derived from images are precise, well-structured, and supported by the information visible in the image. In addition to dataset review, I have experience working with structured data pipelines and automated validation processes using Python, along with tools such as Docker and Git-based workflows. My background allows me to approach AI training tasks with a strong emphasis on accuracy, consistency, and scalability. I am comfortable working independently on remote labeling projects, maintaining high-quality standards, and delivering clear, well-written corrections for AI training datasets.

Labeling Experience

AI Training Data Reviewer & Image Q&A Annotation Specialist

Label StudioImageClassificationObject Detection

This role involved reviewing and improving AI training datasets across both text and image-based tasks. Responsibilities included evaluating question-and-answer pairs derived from images, verifying whether answers accurately reflected visual information, and correcting labeling errors to ensure dataset reliability. The work required extracting relevant details from images, validating AI-generated responses, and producing clear standalone corrections when labels were inaccurate or incomplete. • Reviewed and validated image-based Q&A datasets for AI training • Extracted visual information from images to verify answer accuracy • Corrected labeling inconsistencies and improved dataset quality • Evaluated AI-generated responses for factual accuracy and clarity • Flagged hallucinations, biases, and non-answerable items with explanations

This role involved reviewing and improving AI training datasets across both text and image-based tasks. Responsibilities included evaluating question-and-answer pairs derived from images, verifying whether answers accurately reflected visual information, and correcting labeling errors to ensure dataset reliability. The work required extracting relevant details from images, validating AI-generated responses, and producing clear standalone corrections when labels were inaccurate or incomplete. • Reviewed and validated image-based Q&A datasets for AI training • Extracted visual information from images to verify answer accuracy • Corrected labeling inconsistencies and improved dataset quality • Evaluated AI-generated responses for factual accuracy and clarity • Flagged hallucinations, biases, and non-answerable items with explanations

2021

AI Training Data Annotator & Quality Analyst (NLP)

LabelboxTextEntity Ner ClassificationClassification

Labeled, reviewed, and curated text datasets for machine learning and natural language processing applications. The work involved evaluating AI-generated outputs, applying structured annotation guidelines, and ensuring high-quality training data for model development and evaluation. Emphasis was placed on maintaining annotation consistency, detecting errors or hallucinations in model outputs, and providing structured feedback to improve dataset quality and model performance.

Labeled, reviewed, and curated text datasets for machine learning and natural language processing applications. The work involved evaluating AI-generated outputs, applying structured annotation guidelines, and ensuring high-quality training data for model development and evaluation. Emphasis was placed on maintaining annotation consistency, detecting errors or hallucinations in model outputs, and providing structured feedback to improve dataset quality and model performance.

2018 - 2021

Education

Work History