Jude O.

Key Skills

Software

Scale AI

Surge AI

Appen

Telus

Top Subject Matter

Large Language Model (LLM) RLHF

safety evaluation

code quality rating

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Prompt + Response Writing (SFT)

Object Detection

Evaluation/Rating

Freelancer Overview

Senior AI Data Annotation Specialist (RLHF pairs, instruction-following, red-teaming evaluations). Core strengths include Scale AI, Surge AI, and Appen. Education includes Bachelor of Science, Ambrose Alli University (2023) and Specialization Certificate, DeepLearning.AI / Coursera (2022). AI-training focus includes data types such as Text and labeling workflows including RLHF, Prompt + Response Writing (SFT), and Entity (NER).

Labeling Experience

Senior AI Data Annotation Specialist (RLHF pairs, instruction-following, red-teaming evaluations)

Scale AITextRLHF

Delivered 18,000+ RLHF comparison pairs for a top-3 LLM provider, driving a 97.4% inter-rater agreement score over a 6-month engagement. Led multilingual instruction-following dataset annotation for English, German, and Edo, establishing guidelines used by the wider project team. Performed red-teaming evaluations focused on safety and hallucination detection to inform model policy updates. • Created and validated RLHF pair labels (comparison-style human feedback) for large-scale language modeling. • Conducted code generation quality assessments by rating correctness, efficiency, and style across thousands of model outputs. • Logged and investigated edge cases (340+ flagged) to improve dataset coverage and safety. • Mentored and coordinated a team of 8 annotators on labeling consistency and rubric adherence.

2023 - Present

AI Trainer & Prompt Engineer (SFT-style prompt/data creation)

Surge AITextPrompt + Response Writing (SFT)

Authored 3,500+ diverse adversarial and edge-case prompts to improve instruction-following and fine-tuning readiness of foundational language models. Designed and maintained annotation rubrics to assess creative writing quality, factual accuracy, and reasoning depth. Collaborated with an ML research team to streamline data collection workflows using structured feedback loops. • Wrote and curated prompt datasets with adversarial scenarios for training data generation. • Produced rubric-driven labels to reduce labeler variance and strengthen annotation consistency. • Iterated on prompt and data collection processes to lower annotation cycle time by 35%. • Supported fine-tuning-oriented dataset preparation for ML training pipelines.

2022 - 2022

Data Labeling Analyst (NLP annotation & quality review)

AppenText

Annotated and quality-reviewed 25,000+ NLP samples for search and conversational AI, including sentiment analysis, NER, and semantic similarity labeling. Served as regional quality lead for the West Africa cohort, onboarding and training 15 new annotators on platform standards and labeling consistency. Achieved top 2% accuracy across the global workforce for three consecutive quarters to qualify for elite assignments. • Labeled NLP outputs for sentiment, named entities (NER), and semantic similarity. • Performed quality reviews to validate annotation correctness and adherence to guidelines. • Ran onboarding and training sessions to ensure consistent labeling protocols. • Monitored cohort performance and contributed to sustained high accuracy ratings.

2020 - 2022

Education

Work History