Erald Almoete

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Text

Video

Audio

Top Label Types

Evaluation Rating

Classification

Freelancer Overview

Data-focused professional with a background in Applied Statistics and over 5 years of experience in data quality assurance, validation, and analysis. Experienced in Python (Pandas, NumPy) and advanced Excel for data review and accuracy checks, with hands-on expertise in AI data annotation, LLM output evaluation, and content moderation. Seeking to contribute to high-quality AI training datasets by ensuring accuracy, consistency, and policy compliance in a tech-driven environment.

Labeling Experience

ARI_EN – Real-Time Audio Auditing (Crowdgen/Appen Project)

AppenAudioClassification

Conducted real-time reviews and categorization of audio content to ensure platform and policy compliance. Flagged violations, categorized audio data, and contributed to AI-based moderation systems for enhanced safety. Collaborated remotely with a global moderation/annotation team using defined review protocols. • Supported AI models for audio classification and platform safety monitoring. • Flagged unsafe or inappropriate audio content for review and action. • Used Appen as the primary software for annotation, review, and reporting. • Maintained annotation accuracy and documentation for quality assurance.

Conducted real-time reviews and categorization of audio content to ensure platform and policy compliance. Flagged violations, categorized audio data, and contributed to AI-based moderation systems for enhanced safety. Collaborated remotely with a global moderation/annotation team using defined review protocols. • Supported AI models for audio classification and platform safety monitoring. • Flagged unsafe or inappropriate audio content for review and action. • Used Appen as the primary software for annotation, review, and reporting. • Maintained annotation accuracy and documentation for quality assurance.

2023

Stonecoal V4 – RTC 3PD Content & Ads Evaluation (Crowdgen/Appen Project)

AppenVideoEvaluation Rating

Evaluated third-party advertisement video content on social media platforms for quality, relevance, trust, and policy compliance. Provided structured ratings and flagged misleading or inappropriate ads to improve ad ranking and user experience. Followed annotation guidelines to ensure consistent labeling practices across the project. • Enhanced overall ad platform safety and content quality for users. • Supported machine learning models for ad ranking and trust calculations. • Flagged ads for guideline violations and documented structured feedback. • Worked remotely using Appen's evaluation tools.

Evaluated third-party advertisement video content on social media platforms for quality, relevance, trust, and policy compliance. Provided structured ratings and flagged misleading or inappropriate ads to improve ad ranking and user experience. Followed annotation guidelines to ensure consistent labeling practices across the project. • Enhanced overall ad platform safety and content quality for users. • Supported machine learning models for ad ranking and trust calculations. • Flagged ads for guideline violations and documented structured feedback. • Worked remotely using Appen's evaluation tools.

2023

MOSS – LLM Timeliness Annotation (Crowdgen/Appen Project)

AppenTextEvaluation Rating

Applied standardized annotation guidelines to review time-sensitive labels in large language model (LLM) outputs. Checked LLM responses for time accuracy, missing or incorrect information, and provided corrections to boost model performance. Conducted supporting evidence checks to ensure label accuracy and completeness. • Enhanced the model's ability to process and respond to time-dependent information accurately. • Identified and flagged labeling errors and opportunities for guideline improvement. • Collaborated through Appen's tool with cross-functional annotation teams. • Maintained detailed records for quality auditing and process optimization.

Applied standardized annotation guidelines to review time-sensitive labels in large language model (LLM) outputs. Checked LLM responses for time accuracy, missing or incorrect information, and provided corrections to boost model performance. Conducted supporting evidence checks to ensure label accuracy and completeness. • Enhanced the model's ability to process and respond to time-dependent information accurately. • Identified and flagged labeling errors and opportunities for guideline improvement. • Collaborated through Appen's tool with cross-functional annotation teams. • Maintained detailed records for quality auditing and process optimization.

2023

AI Data Annotator (Independent Contractor) | Crowdgen (Appen)

AppenTextEvaluation Rating

Reviewed and evaluated text, image, video, audio, and LLM-generated content for accuracy, relevance, and cultural appropriateness as an independent contractor at Crowdgen (Appen). Ensured policy compliance, data integrity, and annotation quality while meeting strict project deadlines in a remote setting. Categorized and annotated a wide range of multimodal data to support machine learning, NLP, and generative AI models. • Performed quality audits on annotation work to validate consistency and precision. • Applied annotation guidelines in specialized projects for LLM timeliness and ad quality evaluation. • Moderated and categorized real-time audio, web, and social media content for safety and appropriateness. • Used Appen as the main platform for data labeling, annotation, and review tasks.

Reviewed and evaluated text, image, video, audio, and LLM-generated content for accuracy, relevance, and cultural appropriateness as an independent contractor at Crowdgen (Appen). Ensured policy compliance, data integrity, and annotation quality while meeting strict project deadlines in a remote setting. Categorized and annotated a wide range of multimodal data to support machine learning, NLP, and generative AI models. • Performed quality audits on annotation work to validate consistency and precision. • Applied annotation guidelines in specialized projects for LLM timeliness and ad quality evaluation. • Moderated and categorized real-time audio, web, and social media content for safety and appropriateness. • Used Appen as the main platform for data labeling, annotation, and review tasks.

2023

Education

Work History