Robby R.

Key Skills

Software

Appen

Data Annotation Tech

iMerit

Labelbox

Mercor

Mindrift

OneForma

Remotasks

Scale AI

Toloka

Telus

Top Subject Matter

Biomedical AI / Clinical NLP (disease detection)

AI model evaluation / benchmarking

Top Data Types

Image

3D Sensor

Video

Top Task Types

Diagnosis

Polygon

Bounding Box

Segmentation

Classification

Text Generation

RLHF

Transcription

Computer Programming/Coding

Data Collection

Prompt + Response Writing (SFT)

Question Answering

Text Summarization

Freelancer Overview

AI Researcher – Biomedical Applications (UC San Diego). Brings 10+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, University of Washington (2018) and Bachelor of Science, University of California, San Diego (UCSD) (2020). AI-training focus includes data types such as Medical, DICOM, and Text and labeling workflows including Diagnosis, Evaluation, and Rating.

Labeling Experience

AI Researcher – Biomedical Applications (UCSD)

Text

Developed and applied rigorous evaluation rubrics for AI model assessment using structured datasets and benchmark protocols. Defined quality metrics and testing workflows to ensure consistent labeling and measurement across experiments. Produced documentation of experiment summaries and evaluation results to support iterative model improvement. • Created evaluation rubrics and scoring criteria for labeled outputs • Authored experiment summaries and benchmark documentation • Established reproducible testing workflows • Quality-assured datasets and labeled examples against defined metrics

2019 - 2020

AI Researcher – Biomedical Applications (UC San Diego)

Diagnosis

Responsible for creating disease-detection AI workflows that relied on labeled clinical data and structured annotations. Produced gold-standard examples and dataset artifacts used for evaluation and benchmarking of biomedical NLP systems. Developed labeling and evaluation protocols aligned with experiment documentation and reproducibility requirements. • Labeled/annotated clinical data examples to create training and evaluation sets • Built gold-standard references to support model validation • Authored evaluation benchmarks and methodological guidelines • Defined annotation protocols and quality metrics for dataset integrity

2018 - 2020

Education

Work History