Fabian N.

Key Skills

Software

No software listed

Top Subject Matter

AI model output evaluation and RLHF training feedback for generative language models

Training data annotation and quality assurance for generative AI / LLM datasets with RLHF relevance

Legal Services & Contract Review

Top Data Types

Text

Document

Top Task Types

RLHF

Freelancer Overview

Data Annotation Specialist - Remote. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Education includes Master of Science, University at Buffalo, State University of New York (2023) and Bachelor of Science, University of Central Missouri (2020). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

Data Annotation Specialist - Remote

TextRLHF

No description provided.

2026 - Present

Data Annotation Specialist — remote dataset annotation, review, and QA for generative AI training (Feb 2026–Present)

TextRLHF

Annotated and reviewed complex datasets used to train generative AI systems, emphasizing quality assurance and annotation consistency. Performed validation checks and iterative improvements to support downstream machine learning and language model training. Collaborated with distributed teams to meet project quality targets and maintain labeling reliability. • Conducted dataset annotation and review for generative AI training • Performed quality assurance and consistency maintenance • Contributed to dataset development for ML/LLM projects • Coordinated with distributed teams on quality goals

2026 - Present

AI Research Evaluator (Freelance) — remote AI response evaluation for accuracy, safety, relevance, and consistency (Feb 2025–Sep 2025)

Text

Evaluated AI-generated responses for accuracy, safety, relevance, and consistency using structured annotation guidelines. Logged identified hallucinations, reasoning errors, and policy compliance issues to support RLHF-style training improvements. Provided feedback intended to enhance model performance and dataset quality for language model initiatives. • Assessed response alignment with safety and policy requirements • Applied feedback rubrics and consistency checks per guidelines • Identified factual inconsistencies and reasoning failures • Supported RLHF-based model training efforts via annotated evaluations

2025 - 2025

Education

Work History