Olushoga D.

Key Skills

Software

Scale AI

Don't disclose

Labelbox

Top Subject Matter

LLM output evaluation

instruction-following quality

safety/toxicity assessment

Top Data Types

Text

Image

Document

Top Task Types

Classification

RLHF

Red Teaming

Entity (NER) Classification

Freelancer Overview

Senior AI Data Annotator & LLM Evaluator (Remote, Contract; multiple AI platforms). Core strengths include Scale AI, Don't disclose, and Labelbox. Education includes Bachelor of Arts, N/A (2020). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Senior AI Data Annotator & LLM Evaluator (Remote, Contract; multiple AI platforms)

Scale AIText

Responsible for evaluating thousands of LLM-generated responses for factual accuracy, coherence, tone appropriateness, and strict adherence to provided instructions across general knowledge, STEM, creative writing, and coding domains. Produced structured comparative rankings and written rationales to support RLHF-style feedback and iterative model improvement. Identified, documented, and categorized recurring failure modes such as hallucinations, instruction drift, and unsafe content using standardized evaluation rubrics. • Performed comparative response scoring, ranking, and rating based on rubric criteria • Conducted red-teaming and adversarial prompt testing to surface model vulnerabilities • Flagged toxicity and safety issues and reviewed output consistency against requirements • Calibrated guidelines with QA teams and resolved rubric edge-case disagreements

2022 - Present

Data Annotation Specialist

LabelboxImageClassificationRLHF

No description provided.

2020 - 2022

Data Annotation Specialist (Remote, Contract)

Don't discloseTextClassification

Annotated text (and also image and audio) datasets for supervised machine learning projects including sentiment analysis, named entity recognition, and intent classification. Applied labeling schemas consistently to achieve high inter-annotator agreement and ensure dataset reliability for downstream training. Reviewed peer annotations, flagged inconsistencies, and recommended schema clarifications while adapting to evolving guideline updates across concurrent projects. • Performed structured annotation for sentiment, NER, and intent classification tasks • Maintained high annotation quality with strong inter-annotator agreement • Conducted quality checks of peer work and reported inconsistencies • Updated labeling practices according to changing project guidelines

2020 - 2022

Education

Work History