Merina J.

Key Skills

Software

Other

Appen

Top Subject Matter

AI output evaluation

summarization review

conversational data analysis

Top Data Types

Text

Image

Audio

Top Task Types

Bounding Box

Evaluation/Rating

Classification

Segmentation

Object Detection

Text Generation

Text Summarization

RLHF

Freelancer Overview

AI Data Specialist / AI Training Evaluator at Outlier AI. Core strengths include Other and Appen. Education includes Bachelor of Science, University of Illinois Urbana-Champaign (UIUC) (2019) and AI Data Annotation & Labeling Certificate, N/A (2021). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Entity (NER).

Labeling Experience

AI Data Specialist / AI Training Evaluator at Outlier AI

OtherText

Reviewed AI-generated summaries and conversational outputs against annotation guidelines and accuracy requirements. Evaluated responses using structured scoring rubrics to measure quality and adherence to expectations. Identified and flagged hallucinations, misinformation risks, unsafe content, and bias issues during the evaluation workflow. • Evaluated completeness, accuracy, and guideline compliance for summaries and conversations. • Applied consistent rubric-based scoring to maintain evaluation quality. • Performed hallucination detection and fact/bias risk identification. • Maintained high throughput while meeting quality standards for each task.

2024 - 2025

AI Content Evaluator / Search Quality Rater at TELUS International AI

OtherText

Assessed search results and webpages for relevance and reliability in alignment with user intent. Applied standardized quality criteria and documented evaluation findings for quality assurance consistency. Reported low-quality sources and misleading content to support improvements in search accuracy. • Evaluated relevance, reliability, and user-intent alignment for search outputs. • Used standardized scoring criteria to ensure consistent QA results. • Documented findings to support ongoing quality improvements. • Flagged misleading or low-quality content discovered during review.

2022 - 2024

Data Annotator / NLP Labeling Specialist at Appen

AppenText

Annotated NLP training data for sentiment labeling, entity tagging, intent classification, and topic categorization. Conducted quality assurance checks and corrected inconsistencies to ensure dataset reliability. Maintained productivity targets while adhering to strict accuracy standards for labeled outputs. • Labeled text for sentiment, entities (NER), intents, and topic categories. • Performed QA/error review and corrected labeling inconsistencies. • Followed annotation standards to improve dataset quality for model training. • Met throughput targets without compromising label accuracy.

2020 - 2021

Education

Work History