Joel Osagumwenro

Key Skills

Software

Scale AI

Surge AI

Labelbox

Top Subject Matter

General AI

Llms Domain Expertise

Stem Domain Expertise

Top Data Types

Text

Document

Image

Top Task Types

RLHF

Classification

Freelancer Overview

Data Labeling Specialist (RLHF, QA, Multimodal Labeling). Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI, Surge AI, and Labelbox. Education includes Bachelor of Engineering, University of Benin (UNIBEN) and Certificate in Cloud Engineering, AltSchool Africa. AI-training focus includes data types such as Text, Document, and Computer Code and labeling workflows including RLHF, Classification, and Computer Programming.

Labeling Experience

Data Labeling Specialist (RLHF, QA, Multimodal Labeling)

Scale AITextRLHF

As a data labeling specialist, performed RLHF evaluation and annotated model outputs for training AI systems. Reviewed and scored AI-generated text responses, ensuring adherence to detailed rubrics in technical, code, and general domains. Specialized in instruction-following, helpfulness/harmlessness scoring, and identifying edge cases in STEM-heavy datasets. • Consistently applied labeling guidelines across multiple annotation platforms. • Conducted inter-annotator agreement and quality assurance audits for annotation consistency. • Labeled and evaluated code snippets, bug identification tasks, and technical documentation. • Utilized platforms such as Scale AI, Surge AI, Labelbox, and custom annotation UIs for large-scale labeling workflows.

As a data labeling specialist, performed RLHF evaluation and annotated model outputs for training AI systems. Reviewed and scored AI-generated text responses, ensuring adherence to detailed rubrics in technical, code, and general domains. Specialized in instruction-following, helpfulness/harmlessness scoring, and identifying edge cases in STEM-heavy datasets. • Consistently applied labeling guidelines across multiple annotation platforms. • Conducted inter-annotator agreement and quality assurance audits for annotation consistency. • Labeled and evaluated code snippets, bug identification tasks, and technical documentation. • Utilized platforms such as Scale AI, Surge AI, Labelbox, and custom annotation UIs for large-scale labeling workflows.

2023 - Present

Cybersecurity Annotation Specialist

TextClassification

Provided technical classification and annotation in the cybersecurity domain. Documented and graded vulnerability reports and systematically categorized attack surfaces and findings. Applied established frameworks (OWASP, CVE) for high-precision labeling of ambiguous security data. • Ensured use of accurate STEM terminology in annotation work. • Developed labeling protocols for edge-case, ambiguous scenarios. • Enhanced labeling consistency and reproducibility within security-focused datasets. • Utilized structured rubrics for classification and grading of security scenarios.

Provided technical classification and annotation in the cybersecurity domain. Documented and graded vulnerability reports and systematically categorized attack surfaces and findings. Applied established frameworks (OWASP, CVE) for high-precision labeling of ambiguous security data. • Ensured use of accurate STEM terminology in annotation work. • Developed labeling protocols for edge-case, ambiguous scenarios. • Enhanced labeling consistency and reproducibility within security-focused datasets. • Utilized structured rubrics for classification and grading of security scenarios.

2023 - 2024

Structured Data & Logic Annotator

TextClassification

Performed technical annotation and quality assurance for structured reasoning, logic, and factual verification tasks. Evaluated probabilistic reasoning datasets in the prediction markets domain. Provided consistent, structured labeling to enhance training data integrity for AI reasoning models. • Classified and verified logic-based questions and responses. • Applied domain expertise in probabilistic reasoning annotation. • Ensured consistency across complex training datasets. • Supported quality control in logic and reasoning corpora labeling projects.

Performed technical annotation and quality assurance for structured reasoning, logic, and factual verification tasks. Evaluated probabilistic reasoning datasets in the prediction markets domain. Provided consistent, structured labeling to enhance training data integrity for AI reasoning models. • Classified and verified logic-based questions and responses. • Applied domain expertise in probabilistic reasoning annotation. • Ensured consistency across complex training datasets. • Supported quality control in logic and reasoning corpora labeling projects.

2022 - 2023

Technical Data Annotator (Hardware/Robotics)

DocumentClassification

Produced and evaluated high-quality technical documentation and labeling for hardware systems and robotics datasets. Annotated content in engineering, embedded systems, and IoT, including signal processing and control systems. Leveraged technical expertise to ensure annotation accuracy and integrity for engineering-related AI training data. • Applied engineering knowledge to complex annotation tasks. • Labeled technical schematics, firmware descriptions, and hardware system documentation. • Developed technical annotation guidelines for domain-specific content. • Supported QA for technical corpora with structured rationales and clear documentation.

Produced and evaluated high-quality technical documentation and labeling for hardware systems and robotics datasets. Annotated content in engineering, embedded systems, and IoT, including signal processing and control systems. Leveraged technical expertise to ensure annotation accuracy and integrity for engineering-related AI training data. • Applied engineering knowledge to complex annotation tasks. • Labeled technical schematics, firmware descriptions, and hardware system documentation. • Developed technical annotation guidelines for domain-specific content. • Supported QA for technical corpora with structured rationales and clear documentation.

2022 - 2023

Code Data Labeling Specialist

Labeled and evaluated programming tasks for AI code models including code review, bug identification, and explanation quality rating. Ensured correct annotation of code snippets, code correctness, and documentation completeness for Python, JavaScript, Node.js, shell, and embedded C. Informed by direct platform-building and software engineering experience across the full stack. • Managed annotation of coding tasks in both frontend and backend contexts. • Performed code snippet classification and prompt-response labeling for LLMs. • Supported QA and guideline adherence for code corpus labeling. • Utilized cloud-based labeling platforms and custom annotation pipelines.

Labeled and evaluated programming tasks for AI code models including code review, bug identification, and explanation quality rating. Ensured correct annotation of code snippets, code correctness, and documentation completeness for Python, JavaScript, Node.js, shell, and embedded C. Informed by direct platform-building and software engineering experience across the full stack. • Managed annotation of coding tasks in both frontend and backend contexts. • Performed code snippet classification and prompt-response labeling for LLMs. • Supported QA and guideline adherence for code corpus labeling. • Utilized cloud-based labeling platforms and custom annotation pipelines.

2021 - 2023

Education

Work History