Panagiotis Renieris

Key Skills

Software

Labelbox

Micro1

Toloka

Telus

Top Subject Matter

Medical/Clinical NLP

Clinical Workflow/Pediatric Medicine

Multilingual Medical Content

Top Data Types

Text

Document

Top Task Types

RLHF

Classification

Question Answering

Evaluation Rating

Freelancer Overview

Board-certified Pediatrician (US & EU) with 17+ years of high-acuity clinical experience, currently active as a Medical AI Training SME on platforms including Alignerr/Labelbox, Outlier.ai, and Micro1. Work spans RLHF preference ranking, clinical content review, and medical logic auditing for LLM training pipelines. Clinical background includes complex diagnostic reasoning, high-volume pediatric triage, and institutional safety protocol design — directly transferable to ground truth validation and annotation quality assurance in medical AI datasets. Trilingual (English, Greek, French) with cross-jurisdictional expertise across US, EU, and international healthcare standards.

Labeling Experience

Medical AI Training SME — Freelance

LabelboxTextRLHF

Provided data labeling and annotation services for medical AI training pipelines across multiple platforms. Conducted reinforcement learning from human feedback (RLHF) preference ranking, logic failure flagging, and safety violation detection in clinical LLM output. Authored guideline documents and decision trees for annotation tasks with a focus on clinical and bilingual medical text. • Delivered structured and validated data for healthcare AI. • Performed entity recognition, classification, and intent annotation on medical text. • Executed cross-lingual annotation in English and Greek. • Used annotation software including Labelbox, Outlier.ai, and Micro1.

Provided data labeling and annotation services for medical AI training pipelines across multiple platforms. Conducted reinforcement learning from human feedback (RLHF) preference ranking, logic failure flagging, and safety violation detection in clinical LLM output. Authored guideline documents and decision trees for annotation tasks with a focus on clinical and bilingual medical text. • Delivered structured and validated data for healthcare AI. • Performed entity recognition, classification, and intent annotation on medical text. • Executed cross-lingual annotation in English and Greek. • Used annotation software including Labelbox, Outlier.ai, and Micro1.

2026 - Present

Independent Medical Reviewer & Clinical Logic Architect

TextClassification

Designed and validated clinical SOP frameworks and logic gates mirroring annotation guidelines for workflow automation. Created ground truth data sets for high-acuity pediatric clinical decision training. Supported development and quality assurance of labeled protocols and workflow guidance. • Authored medical annotation and ground truth guidelines. • Structured logic for safe and accurate clinical annotation. • Enabled high-reliability data-driven protocols. • Delivered annotated content for AI and staff training.

Designed and validated clinical SOP frameworks and logic gates mirroring annotation guidelines for workflow automation. Created ground truth data sets for high-acuity pediatric clinical decision training. Supported development and quality assurance of labeled protocols and workflow guidance. • Authored medical annotation and ground truth guidelines. • Structured logic for safe and accurate clinical annotation. • Enabled high-reliability data-driven protocols. • Delivered annotated content for AI and staff training.

2013 - Present

Education

Work History