Muhammad A.

Key Skills

Software

Other

Top Subject Matter

AI safety evaluation

harmful content identification

adversarial prompting (LLM research)

Top Data Types

Text

Computer Code Programming

Document

Top Task Types

Fine-tuning

Entity (NER) Classification

Freelancer Overview

AI Data Labeling & Safety Evaluation Contributor — HausaNLP Research Foundation (collaboration with Google Research). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Science, Bayero University Kano (2026). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

AI & Robotics Intern — National Centre for Artificial Intelligence and Robotics (NCAIR)

OtherFine-tuning

Worked as an AI & Robotics Intern with instruction aimed at strengthening AI/ML foundations that support training workflows. Built practical understanding of machine learning and embedded/robotics concepts relevant to downstream AI development. Applied training knowledge through hands-on work on AI and intelligent systems. • Python and advanced data science training • Machine learning foundation development for AI training workflows • Practical work on AI and intelligent systems concepts • Integration of AI concepts with embedded/robotics learning

2025 - 2026

AI Safety & Adversarial Prompt Generation Contributor - HausaNLP Research Foundation

Entity (NER) Classification

You contributed to AI safety evaluation and adversarial prompt generation initiatives with the HausaNLP research effort. You worked on testing large language models for harmful or unsafe outputs and organizing the resulting findings. You supported dataset categorization and harmful content identification using evaluation and compliance-oriented workflows. • Participated in adversarial prompt generation • Crafted prompts to elicit unsafe model outputs • Evaluated and categorized outputs for safety compliance • Contributed to dataset categorization and harmful content identification

2024 - 2024

AI Data Labeling & Safety Evaluation Contributor — HausaNLP Research Foundation (collaboration with Google Research)

Text

Contributed to AI safety evaluation by crafting adversarial prompts and assessing LLM outputs for harmful or unsafe content. Evaluated and categorized AI-generated responses to determine safety compliance. Participated in dataset categorization and harmful content identification tasks for research purposes. • Adversarial prompt generation targeting harmful or unsafe outputs • Safety evaluation and categorization of model-generated text • Dataset categorization and harmful content identification • Compliance-focused review of safety outputs

2024

Education

Work History