Malik Jones

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Classification

RLHF

Evaluation Rating

Prompt Response Writing SFT

Entity Ner Classification

Question Answering

Text Summarization

Red Teaming

Freelancer Overview

I am an experienced AI training data specialist with over two years supporting machine learning workflows through data labeling, annotation, and quality assurance. My background includes hands-on annotation and review of text, image, and multi-modal datasets, with a focus on adhering to detailed taxonomies and annotation guidelines. I have evaluated AI-generated content for accuracy, bias, and policy compliance, and have contributed to the creation of high-quality datasets used in natural language processing and e-commerce domains. My strengths include meticulous attention to detail, strong analytical skills, and the ability to maintain high productivity in remote, performance-driven environments. I am also skilled in linguistic quality assurance, content proofreading, and data validation, ensuring reliable and scalable training data for AI models.

Labeling Experience

Content Review, Moderation, and Data Quality Assurance

AppenTextClassificationEvaluation Rating

Reviewed and labeled user-generated and AI-generated content according to safety, compliance, and quality guidelines. Identified policy violations, categorized sensitive content, and evaluated responses for alignment with platform standards. Conducted quality assurance checks to detect labeling inconsistencies and improve dataset reliability. Supported AI safety efforts by flagging edge cases and contributing to guideline refinements.

Reviewed and labeled user-generated and AI-generated content according to safety, compliance, and quality guidelines. Identified policy violations, categorized sensitive content, and evaluated responses for alignment with platform standards. Conducted quality assurance checks to detect labeling inconsistencies and improve dataset reliability. Supported AI safety efforts by flagging edge cases and contributing to guideline refinements.

2025 - 2025

NLP Data Annotation for Customer Support AI Systems

AppenTextEntity Ner ClassificationClassification

Annotated customer support conversations and tickets to train NLP models used in automated support systems. Tasks included intent classification, sentiment labeling, named entity recognition (NER), and summarization of customer issues and resolutions. Leveraged customer support experience to accurately interpret user intent, identify key entities, and apply context-aware labeling. Ensured high-quality annotations by following detailed project guidelines and participating in QA review cycles.

Annotated customer support conversations and tickets to train NLP models used in automated support systems. Tasks included intent classification, sentiment labeling, named entity recognition (NER), and summarization of customer issues and resolutions. Leveraged customer support experience to accurately interpret user intent, identify key entities, and apply context-aware labeling. Ensured high-quality annotations by following detailed project guidelines and participating in QA review cycles.

2024 - 2025

AI-Generated Text Evaluation and RLHF Annotation

AppenTextClassificationRLHF

Annotated customer support conversations and tickets to train NLP models used in automated support systems. Tasks included intent classification, sentiment labeling, named entity recognition (NER), and summarization of customer issues and resolutions. Leveraged customer support experience to accurately interpret user intent, identify key entities, and apply context-aware labeling. Ensured high-quality annotations by following detailed project guidelines and participating in QA review cycles.

Annotated customer support conversations and tickets to train NLP models used in automated support systems. Tasks included intent classification, sentiment labeling, named entity recognition (NER), and summarization of customer issues and resolutions. Leveraged customer support experience to accurately interpret user intent, identify key entities, and apply context-aware labeling. Ensured high-quality annotations by following detailed project guidelines and participating in QA review cycles.

2023 - 2024

Education

Work History