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J
John M.

John M.

United Kingdom flagStroud, England

Key Skills

Software

Google Cloud Vertex AIGoogle Cloud Vertex AI
Data Annotation TechData Annotation Tech
CloudFactoryCloudFactory

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code ProgrammingComputer Code Programming
TextText
AudioAudio

Top Task Types

Computer Programming/CodingComputer Programming/Coding
Text GenerationText Generation
Question AnsweringQuestion Answering
Text SummarizationText Summarization
Fine-tuningFine-tuning
Data CollectionData Collection
Evaluation/RatingEvaluation/Rating
TranscriptionTranscription

Freelancer Overview

I get how crucial high-quality data is for building solid models because I work with data pipelines and automation every day. My background in data analysis and development means I don’t just look at labeling as a repetitive task; I approach it with an analytical mindset. I’m used to cleaning up messy datasets, parsing through API logs, and organizing structured information, so I have a natural eye for the kind of edge cases, inconsistencies, and biases that can easily throw a model off track. What really sets me apart is that I genuinely enjoy the technical precision it takes to get data right. Whether I’m annotating text for conversational bots, handling complex technical data, or evaluating model responses for accuracy, I focus heavily on context and detail. I’m comfortable adapting to strict guidelines quickly, and my workflow is built around efficiency and high-quality throughput, so you can count on data that is actually ready to use.

Labeling Experience

Programming

Computer Code ProgrammingComputer Code ProgrammingComputer Programming/CodingComputer Programming/Coding

I handle codebase annotation and code evaluation for programming datasets, specifically focused on reviewing, labeling, and debugging scripts to train AI models. My day-to-day involves breaking down complex code blocks, verifying logic, and ensuring syntax accuracy across different frameworks. Because I manually test edge cases and enforce strict quality checks, the resulting dataset is highly accurate, clean, and optimized for training robust, code-generation models

2019 - Present