Faith Oke

Key Skills

Software

Label Studio

Doccano

Mercor

Lionbridge

OneForma

Scale AI

Top Subject Matter

Multilingual NLP and Large Language Models

Conversational AI Evaluation

Natural Language Processing - Classification and Moderation

Top Data Types

Text

Audio

Document

Top Task Types

RLHF

Classification

Transcription

Evaluation/Rating

Text Generation

Freelancer Overview

Freelance AI Trainer & Data Labeling Specialist. Core strengths include Label Studio, Doccano, and Internal. Education includes Bachelor's Degree, N/A. AI-training focus includes data types such as Text and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

Freelance AI Trainer & Data Labeling Specialist

Label StudioTextRLHF

As a freelance AI Trainer and Data Labeling Specialist at Hansem Global, I managed diverse annotation and evaluation tasks for language model training. My responsibilities included executing RLHF workflows, assessing prompt-response interactions, and ensuring high annotation quality. I consistently maintained best-practice QA standards across large-scale multilingual text datasets. • Performed over 5,000 annotation and evaluation tasks for multilingual text data • Executed pairwise comparison, preference ranking, and reward data generation • Flagged hallucinations, bias, and content safety issues in AI outputs • Used Label Studio and Doccano for detailed annotation and QA

As a freelance AI Trainer and Data Labeling Specialist at Hansem Global, I managed diverse annotation and evaluation tasks for language model training. My responsibilities included executing RLHF workflows, assessing prompt-response interactions, and ensuring high annotation quality. I consistently maintained best-practice QA standards across large-scale multilingual text datasets. • Performed over 5,000 annotation and evaluation tasks for multilingual text data • Executed pairwise comparison, preference ranking, and reward data generation • Flagged hallucinations, bias, and content safety issues in AI outputs • Used Label Studio and Doccano for detailed annotation and QA

2024 - 2026

AI Response Evaluator & Quality Analyst

Text

As an AI Response Evaluator and Quality Analyst at Uber AI, I evaluated, ranked, and annotated model outputs for A/B testing and benchmarking. My work focused on ensuring factuality, safety, and user alignment in conversational AI systems. I supported reward modeling and contributed to improving evaluation guidelines for ambiguous and edge cases. • Performed 2,000+ response evaluations using standardized scoring rubrics • Supplied high-quality preference data for reward model training • Benchmarked model output reliability and user alignment • Utilized proprietary evaluation dashboards and QA systems

As an AI Response Evaluator and Quality Analyst at Uber AI, I evaluated, ranked, and annotated model outputs for A/B testing and benchmarking. My work focused on ensuring factuality, safety, and user alignment in conversational AI systems. I supported reward modeling and contributed to improving evaluation guidelines for ambiguous and edge cases. • Performed 2,000+ response evaluations using standardized scoring rubrics • Supplied high-quality preference data for reward model training • Benchmarked model output reliability and user alignment • Utilized proprietary evaluation dashboards and QA systems

2023 - 2024

Linguistic Reviewer & QA Specialist

Label StudioTextClassification

As a Linguistic Reviewer and QA Specialist at Quelsh, I carried out large-scale labeling and validation for Natural Language Processing tasks. My primary tasks included annotation for classification, intent detection, and moderation, with a focus on accuracy and label consistency. I conducted multiple dataset QA cycles to identify inconsistencies and maintain annotation quality across structured and unstructured datasets. • Labeled and validated over 10,000 data points for NLP classification • Detected and corrected annotation errors through QA reviews • Ensured guideline compliance throughout labeling processes • Annotated data from various domains including e-commerce and support

As a Linguistic Reviewer and QA Specialist at Quelsh, I carried out large-scale labeling and validation for Natural Language Processing tasks. My primary tasks included annotation for classification, intent detection, and moderation, with a focus on accuracy and label consistency. I conducted multiple dataset QA cycles to identify inconsistencies and maintain annotation quality across structured and unstructured datasets. • Labeled and validated over 10,000 data points for NLP classification • Detected and corrected annotation errors through QA reviews • Ensured guideline compliance throughout labeling processes • Annotated data from various domains including e-commerce and support

2022 - 2023

Education

Work History