Marian S.

Key Skills

Software

Scale AI

Don't disclose

Lionbridge

Top Subject Matter

Healthcare-focused LLM evaluation and annotation

Clinical/healthcare data quality annotation

Search relevance labeling for healthcare/wellness

Top Data Types

Text

Document

Top Task Types

Entity (NER) Classification

Freelancer Overview

AI Training & Evaluation Specialist — Scale AI (2021–2024). Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, Don't disclose, and Lionbridge. Education includes Doctor of Health Science, Purdue University Global (2025) and Master of Public Health, University of California, Los Angeles (2017). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

AI Training & Evaluation Specialist — Scale AI (2021–2024)

Scale AIText

Evaluated large language model outputs for accuracy, factual consistency, safety compliance, and overall response quality in healthcare-focused contexts. Supported the improvement of dataset reliability through iterative annotation and refinement workflows aligned to prompt/rubric expectations. Ensured outputs met quality and safety criteria before downstream use. • Assessed LLM responses against reference facts and consistency criteria • Flagged potential safety concerns and non-compliant content • Participated in annotation pipeline contributions and workflow refinement • Worked with healthcare-related datasets to improve reasoning reliability

2021 - 2024

Healthcare Data Quality Annotator — Akorbi / Google Health (2019–2021)

Don't discloseEntity (NER) Classification

Reviewed structured clinical data and healthcare documentation to improve dataset consistency and support machine learning performance. Applied medical terminology knowledge to validate and normalize terminology for downstream model use. Contributed to annotation quality processes for healthcare-related machine learning applications. • Checked structured records for consistency and annotation correctness • Validated medical terminology within clinical documentation • Improved dataset consistency for ML training and evaluation • Supported model performance across medical applications

2019 - 2021

Search Relevance Annotator — RWS / Lionbridge AI (2017–2019)

LionbridgeText

Assessed search query relevance and content quality for healthcare, wellness, and medical technology topics. Used evaluation criteria to determine whether content matched user intent and improved relevance accuracy. Supported training/evaluation of systems that rely on relevance judgments. • Evaluated query-to-result topical alignment and relevance • Judged content quality according to task guidelines • Helped improve search ranking accuracy and intent matching • Performed ongoing quality checks as part of annotation workstreams

2017 - 2019

Education

Work History