Anita I.

Key Skills

Software

Appen

Labelbox

Scale AI

Top Subject Matter

RLHF/LLM training data annotation

LLM evaluation and preference/ranking annotation

NLP training data annotation (multi-task)

Top Data Types

Image

Video

Audio

Top Task Types

RLHF

Classification

Freelancer Overview

AI Training Data Specialist | Outlier AI. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Outlier AI, Handshake, and Open Train AI. Education includes Bachelor of Engineering, Lead City University (2022). AI-training focus includes data types such as Text and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

AI Training Data Specialist | Outlier AI

TextRLHF

Performed text classification and response ranking to create RLHF training data for large language models and AI systems. Annotated AI-generated outputs for correctness, tone, safety, and instruction-following accuracy using detailed rubrics across large volumes. Ensured dataset integrity by identifying edge cases, ambiguous prompts, and low-quality outputs while meeting strict turnaround timelines. • Generated training labels via classification and response ranking tasks for RLHF workflows • Evaluated outputs on multiple dimensions including correctness, safety, and intent adherence • Applied rubric-based guidance to maintain high inter-annotator agreement • Flagged edge cases and inconsistencies to protect training data reliability

2025 - 2026

AI Content Annotator | Open Train AI

TextClassification

Labeled and categorized AI-generated text for multiple NLP task types including summarization, question answering, and instruction following. Performed multi-label classification and sentiment annotation to enrich training datasets for NLP model development. Maintained accuracy benchmarks and submitted well-documented labeled outputs within remote asynchronous workflows. • Produced labels for summarization, QA, and instruction-following data • Applied multi-label classification and sentiment tagging • Consistently met guideline-driven quality benchmarks • Collaborated asynchronously and provided rationale-backed labeled data

2026

Data Annotation Contributor | Handshake

Text

Contributed to structured annotation of AI responses, including text labeling, preference ranking, and binary classification tasks. Assessed model outputs for factual accuracy, coherence, and alignment with user intent to support curated training datasets. Documented annotation errors and inconsistencies to improve quality assurance for training data pipelines. • Performed preference ranking and binary classification over AI response sets • Evaluated factual accuracy, coherence, and intent alignment • Applied consistent labeling criteria across diverse prompt types • Reported discrepancies to support QA processes for dataset quality

2026

Education

Work History