Martha Isabel Ferreira

Key Skills

Software

Appen

Top Subject Matter

Spanish linguistics

Pronunciation Domain Expertise

Grammar Domain Expertise

Top Data Types

Image

Text

Video

Audio

Document

Top Label Types

Classification

Object Detection

Translation Localization

Freelancer Overview

AI Linguistic Evaluator (Freelance - AI Data Platform). Core strengths include Internal, Proprietary Tooling, and Appen. AI-training focus includes data types such as Audio and Text and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

AI Linguistic Evaluator (Freelance - AI Data Platform)

Audio

Reviewed and evaluated neutral Spanish audio data for use in AI model training. Focused on detecting pronunciation, intonation, and language usage errors to ensure linguistic quality. Maintained strict adherence to confidentiality and quality guidelines throughout the labeling process. • Conducted multiple tasks with an average of 20 audio samples per task. • Applied phonetics and language expertise for detailed error analysis. • Consistently validated content against outlined standards before submission. • Used proprietary or internal AI data platform software to complete tasks.

Reviewed and evaluated neutral Spanish audio data for use in AI model training. Focused on detecting pronunciation, intonation, and language usage errors to ensure linguistic quality. Maintained strict adherence to confidentiality and quality guidelines throughout the labeling process. • Conducted multiple tasks with an average of 20 audio samples per task. • Applied phonetics and language expertise for detailed error analysis. • Consistently validated content against outlined standards before submission. • Used proprietary or internal AI data platform software to complete tasks.

2025 - Present

AI Content Evaluator (Project-based - CROWDGEN BY APPEN - AI Data Platform)

AppenTextClassification

Classified emails and text messages according to type for AI model intent training. Assessed and labeled content for scam, spam, suspicious, or legitimate status. Applied and upheld predetermined quality standards in each annotation session. • Reviewed and analyzed large volumes of text content for intent classification. • Labeled textual data to support algorithm development for fraud and spam detection. • Ensured compliance with project policies and evaluation criteria. • Used Appen-provided AI data platform tools for all annotation work.

Classified emails and text messages according to type for AI model intent training. Assessed and labeled content for scam, spam, suspicious, or legitimate status. Applied and upheld predetermined quality standards in each annotation session. • Reviewed and analyzed large volumes of text content for intent classification. • Labeled textual data to support algorithm development for fraud and spam detection. • Ensured compliance with project policies and evaluation criteria. • Used Appen-provided AI data platform tools for all annotation work.

2026 - 2026

Education

Work History