Byron Morara

Key Skills

Software

Encord

Remotasks

Top Subject Matter

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Top Data Types

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Top Label Types

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Freelancer Overview

I am a detail-oriented data annotator and AI data trainer with hands-on experience supporting computer vision and NLP projects for large-scale AI development. My background in Mathematics and Computer Science, combined with practical roles at CloudFactory and Invisible Technologies, has equipped me with expertise in image annotation (bounding boxes, segmentation, classification), prompt engineering, and rubric-based evaluation of AI responses. I am proficient in Python, SQL, R, and various labeling tools such as Encord, and have contributed to maintaining high data quality through peer review, QA collaboration, and workflow optimization. My commitment to accuracy, adaptability, and continuous learning enables me to deliver high-quality datasets that drive effective machine learning outcomes.

Labeling Experience

AI Image Annotator

EncordImageBounding Box

As an AI Image Annotator, large-scale image datasets are annotated to support computer vision and object detection models. Bounding box, segmentation, and classification annotations are performed with high precision and consistency. Data review and peer validation ensure integrity of the dataset throughout the workflow. • Utilized Encord and other labeling tools for annotation tasks. • Collaborated with QA and project managers to optimize annotation guidelines. • Reviewed and flagged inconsistencies in peer annotations. • Implemented workflow improvements to boost throughput and accuracy.

As an AI Image Annotator, large-scale image datasets are annotated to support computer vision and object detection models. Bounding box, segmentation, and classification annotations are performed with high precision and consistency. Data review and peer validation ensure integrity of the dataset throughout the workflow. • Utilized Encord and other labeling tools for annotation tasks. • Collaborated with QA and project managers to optimize annotation guidelines. • Reviewed and flagged inconsistencies in peer annotations. • Implemented workflow improvements to boost throughput and accuracy.

2025

AI Data Trainer

TextEvaluation Rating

As an AI Data Trainer, AI model responses in STEM, language, and reasoning domains are evaluated for quality and accuracy. Rubric-based scoring is applied to ensure factual, structured, and clear AI outputs. Training prompts are generated and reviewed for enhanced diversity and contextual relevance in datasets. • Scored AI responses using standardized rubrics for multiple domains. • Enhanced datasets by creating and refining contextual prompts. • Maintained high standards of clarity and factual accuracy in evaluation. • Contributed to overall dataset quality for AI training.

As an AI Data Trainer, AI model responses in STEM, language, and reasoning domains are evaluated for quality and accuracy. Rubric-based scoring is applied to ensure factual, structured, and clear AI outputs. Training prompts are generated and reviewed for enhanced diversity and contextual relevance in datasets. • Scored AI responses using standardized rubrics for multiple domains. • Enhanced datasets by creating and refining contextual prompts. • Maintained high standards of clarity and factual accuracy in evaluation. • Contributed to overall dataset quality for AI training.

2025 - 2025

AI Data Trainer (Freelance, Part-Time)

RemotasksTextEvaluation Rating

In a freelance capacity as an AI Data Trainer, text-based prompt-response evaluations were conducted for AI language models. Output refinements were performed to align with key project objectives and linguistic standards. Feedback was relayed to QA leads to facilitate improved model interpretability and response quality. • Evaluated AI model outputs against project benchmarks. • Improved linguistic accuracy and coherence of responses. • Provided quality-oriented input for enhancing datasets. • Supported prompt-response development for diverse language tasks.

In a freelance capacity as an AI Data Trainer, text-based prompt-response evaluations were conducted for AI language models. Output refinements were performed to align with key project objectives and linguistic standards. Feedback was relayed to QA leads to facilitate improved model interpretability and response quality. • Evaluated AI model outputs against project benchmarks. • Improved linguistic accuracy and coherence of responses. • Provided quality-oriented input for enhancing datasets. • Supported prompt-response development for diverse language tasks.

2023 - 2024

Education

Work History