Mark M.

Key Skills

Software

Remotasks

Appen

Top Subject Matter

AI/ML training data annotation and quality assurance

Computer vision and multimodal AI dataset labeling

Top Data Types

Text

Image

Top Task Types

RLHF

Entity (NER) Classification

Freelancer Overview

AI Data Annotator & Reviewer (Remotasks AI / Outlier AI). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Remotasks and Appen. Education includes Associate of Science, City College of San Francisco (CCSF) (2025) and Bachelor of Science, Arizona State University (2023). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

Data Annotation Specialist (Appen)

AppenImage

Conducted large-scale data labeling for image, text, video, and audio datasets as part of AI training workflows. Performed quality audits and peer reviews to assess label accuracy and dataset completeness. Collaborated with AI teams on data validation and cleaning to improve overall dataset quality. • Image/text/video/audio data labeling • AI output review and quality assurance • Data validation and cleaning • Peer review and quality audits

2024 - Present

Data Annotation Specialist - Appen

AppenImageRLHFEntity (NER) Classification

Conducted large-scale data labeling for image, text, and audio datasets. Performed quality audits and peer reviews to maintain labeling standards. Collaborated with AI teams on dataset validation and cleaning to improve training readiness. • Labeled and annotated multimodal datasets according to guidelines • Reviewed outputs for quality, accuracy, and consistency • Validated and cleaned data for improved model training support • Participated in QA workflows including peer review and audits

2024 - 2025

AI Data Annotator & Reviewer (Remotasks AI / Outlier AI)

RemotasksText

Performed data annotation and labeling tasks across various AI projects while following provided labeling guidelines. Reviewed and validated AI-generated outputs to ensure accuracy and dataset quality. Maintained consistent, high-accuracy results to support model training and improvement. • Data annotation and labeling per task instructions • Output review and validation for correctness • Quality assurance against guidelines • Consistency checks across large-scale dataset work

2022 - 2024

Education

Work History