Fernando Interiano

Key Skills

Software

Scale AI

Appen

Mercor

Lionbridge

Top Subject Matter

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Top Data Types

Text

Image

Top Label Types

Classification

Evaluation Rating

Entity Ner Classification

Freelancer Overview

I am a PhD-trained data labeling and AI data quality specialist with deep experience designing annotation frameworks, optimizing labeling workflows, and ensuring high dataset accuracy for machine learning projects. My background spans NLP, computer vision, and multimodal AI systems, where I have led large-scale annotation initiatives, developed comprehensive labeling guidelines, and conducted rigorous quality audits. I am skilled in Python, SQL, and a variety of data analysis tools, and have collaborated closely with AI researchers, data scientists, and engineering teams to deliver reliable, high-quality training data. My work has contributed to published research on reducing labeling noise and optimizing data quality, and I am passionate about advancing data-centric AI through precise annotation, robust validation, and continuous workflow improvement.

Labeling Experience

Machine Learning Data Researcher (Graduate Research Assistant)

MercorTextEvaluation Rating

As a Machine Learning Data Researcher, investigated dataset bias, labeling noise, and annotation reliability. Developed validation techniques and quality metrics to improve ML dataset integrity. Published studies on data-centric AI and annotation processes. • Analyzed labeling errors through dedicated research • Created quality assessment metrics for annotation • Supported model experimentation with improved data pipelines • Published research on scalable annotation methods

As a Machine Learning Data Researcher, investigated dataset bias, labeling noise, and annotation reliability. Developed validation techniques and quality metrics to improve ML dataset integrity. Published studies on data-centric AI and annotation processes. • Analyzed labeling errors through dedicated research • Created quality assessment metrics for annotation • Supported model experimentation with improved data pipelines • Published research on scalable annotation methods

2019 - 2022

Senior Data Labeling & Annotation Specialist

Scale AITextClassification

As Senior Data Labeling & Annotation Specialist at Scale AI, designed and managed large-scale workflows for NLP and computer vision. Focused on developing labeling guidelines and conducting quality audits for dataset consistency. Coordinated with ML teams to train and supervise annotators for increased productivity. • Implemented annotation frameworks supporting multiple AI domains • Conducted detailed error analysis and dataset refinement • Developed comprehensive rubrics for annotation accuracy • Optimized multi-team supervision processes

As Senior Data Labeling & Annotation Specialist at Scale AI, designed and managed large-scale workflows for NLP and computer vision. Focused on developing labeling guidelines and conducting quality audits for dataset consistency. Coordinated with ML teams to train and supervise annotators for increased productivity. • Implemented annotation frameworks supporting multiple AI domains • Conducted detailed error analysis and dataset refinement • Developed comprehensive rubrics for annotation accuracy • Optimized multi-team supervision processes

2022

AI Data Quality Analyst

AppenTextClassification

Worked as an AI Data Quality Analyst at Appen, focusing on dataset validation and annotation quality. Ensured accuracy and consistency in labeled data across diverse ML projects. Supported multilingual labeling initiatives and taxonomy design. • Conducted quality assurance and conflict resolution for labeling accuracy • Assisted in building robust data categorization systems • Made targeted data corrections for model improvements • Worked on multilingual annotation and validation tasks

Worked as an AI Data Quality Analyst at Appen, focusing on dataset validation and annotation quality. Ensured accuracy and consistency in labeled data across diverse ML projects. Supported multilingual labeling initiatives and taxonomy design. • Conducted quality assurance and conflict resolution for labeling accuracy • Assisted in building robust data categorization systems • Made targeted data corrections for model improvements • Worked on multilingual annotation and validation tasks

2020 - 2022

Data Annotation Specialist

LionbridgeImageEntity Ner Classification

Served as Data Annotation Specialist at Lionbridge AI, labeling and validating datasets for NLP and image recognition. Applied guidelines for entity recognition, classification, and sentiment annotation. Maintained high accuracy while supporting feedback and guideline refinement. • Managed NLP and image dataset annotation for varied projects • Applied labeling criteria for entity recognition tasks • Upheld quality standards across annotation teams • Contributed to feedback loops improving guidelines

Served as Data Annotation Specialist at Lionbridge AI, labeling and validating datasets for NLP and image recognition. Applied guidelines for entity recognition, classification, and sentiment annotation. Maintained high accuracy while supporting feedback and guideline refinement. • Managed NLP and image dataset annotation for varied projects • Applied labeling criteria for entity recognition tasks • Upheld quality standards across annotation teams • Contributed to feedback loops improving guidelines

2018 - 2020

Education

Work History