Frida S.

Key Skills

Software

AWS SageMaker

Appen

Clickworker

Top Subject Matter

Nlp Domain Expertise

Generative AI

Llms Domain Expertise

Top Data Types

Text

Image

Video

Top Task Types

Prompt + Response Writing (SFT)

Data Collection

Bounding Box

Segmentation

Entity (NER) Classification

Evaluation/Rating

Freelancer Overview

I have experience in data annotation, training data quality review, and AI output evaluation, with familiarity in common tasks such as text classification, question-answer labeling, intent recognition, content moderation, and instruction-following assessment. I am able to work strictly within project guidelines while maintaining strong consistency and accuracy. I am particularly skilled at understanding task objectives quickly, identifying edge cases, ambiguous samples, and abnormal data, and improving overall dataset quality through careful review, correction, and guideline-based judgment. My strengths are attention to detail, stable execution, and strong language comprehension, which make me well suited for AI training data work that requires high accuracy and consistency. I can evaluate model responses for relevance, correctness, fluency, and safety, identify low-quality outputs and potential risk points, and summarize recurring issues to support better annotation standards and improved training results. I also adapt quickly to different AI data projects and work effectively in structured, quality-focused workflows.

Labeling Experience

AI Data Trainer / AI Annotation Specialist

Text

As an AI Data Trainer and Annotation Specialist, I annotated and reviewed large-scale text datasets for NLP and generative AI training with high accuracy. I evaluated and rated model outputs for relevance, factuality, safety, and fluency. I refined prompts, guidelines, and documentation to improve model performance and labeling quality. • Annotated and reviewed large-scale text datasets for NLP and generative AI. • Evaluated model outputs for instruction-following quality and safety. • Refined prompts and annotation guidelines to improve labeling consistency. • Identified and documented edge cases and failure patterns for model improvement.

2024 - Present

Prompt and Annotation Optimization Project Contributor

TextPrompt + Response Writing (SFT)

I participated in optimizing prompts and annotation strategies for enhancing model instruction-following and summarization. I created diverse prompt variations and clarified project examples to support consistent data annotation. My efforts led to improved reasoning and task performance of LLMs in evaluation cycles. • Created and tested prompt variations for reasoning tasks. • Updated and clarified project annotation guidelines. • Enhanced annotation consistency across team members. • Supported SFT for improved model instruction following.

2023 - 2024

LLM Response Evaluation Project Contributor

Text

As part of a team conducting LLM response evaluations, I ranked and compared AI-generated text responses across helpfulness, coherence, and accuracy. I flagged hallucinations, unsafe content, and low-quality generations for analysis. I contributed to quality assurance in generative model outputs for safer and more reliable AI systems. • Ranked and evaluated LLM-generated responses for quality criteria. • Identified and flagged unsafe or unhelpful responses. • Ensured alignment with project safety and factuality policies. • Collaborated with team members to refine evaluation criteria.

2023 - 2024

Education

Work History