Innasia Johnson

Key Skills

Software

Surge AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Label Types

Polygon

Point Key Point

RLHF

Computer Programming Coding

Freelancer Overview

I am a detail-oriented AI training and data annotation professional with hands-on experience supporting machine learning model development through high-quality labeling, content evaluation, and structured annotation tasks. My background includes text classification, intent labeling, sentiment analysis, and search relevance evaluation, with a strong focus on NLP and data science workflows. I excel at interpreting complex guidelines, ensuring data quality, and maintaining accuracy and consistency across large datasets. Skilled in using AI training and annotation platforms, web-based tools, and spreadsheet systems, I am adept at remote work, meeting productivity benchmarks, and contributing to the improvement of AI systems through diligent data handling and quality assurance.

Labeling Experience

DATA LABELING

Surge AIComputer Code ProgrammingPolygonPoint Key Point

Data Labeling / AI Training Experience Text Classification & Content Labeling Projects Labeled large volumes of text data for category classification, topic tagging, and content moderation tasks. Ensured consistency with detailed guidelines and resolved ambiguous cases through careful analysis. Intent Labeling & Sentiment Analysis (NLP-focused) Annotated user queries and conversational data to identify user intent, sentiment polarity, and contextual meaning, supporting the training and evaluation of NLP and conversational AI models. Search Relevance & Query Evaluation Evaluated search engine outputs by assessing relevance, usefulness, and alignment with user intent. Applied structured rating criteria to improve ranking quality and search accuracy. AI Response Review & Quality Assurance Reviewed AI-generated outputs for correctness, coherence, and guideline compliance. Flagged errors, edge cases, and reasoning gaps to support iterative model improvement. Data Validation & Annotatio

Data Labeling / AI Training Experience Text Classification & Content Labeling Projects Labeled large volumes of text data for category classification, topic tagging, and content moderation tasks. Ensured consistency with detailed guidelines and resolved ambiguous cases through careful analysis. Intent Labeling & Sentiment Analysis (NLP-focused) Annotated user queries and conversational data to identify user intent, sentiment polarity, and contextual meaning, supporting the training and evaluation of NLP and conversational AI models. Search Relevance & Query Evaluation Evaluated search engine outputs by assessing relevance, usefulness, and alignment with user intent. Applied structured rating criteria to improve ranking quality and search accuracy. AI Response Review & Quality Assurance Reviewed AI-generated outputs for correctness, coherence, and guideline compliance. Flagged errors, edge cases, and reasoning gaps to support iterative model improvement. Data Validation & Annotatio

2022 - 2024

Education

Work History