Muhammad K.

Key Skills

Software

Don't disclose

Top Subject Matter

STEM reasoning and technical documentation (Civil Engineering, math, symbolic logic)

Technical writing

STEM problem solving

Top Data Types

Text

Document

Top Task Types

Text Generation

RLHF

Freelancer Overview

AI Technical Content Evaluator & Subject Matter Expert. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Bachelor of Science, Bayero University Kano (BUK) (2025). AI-training focus includes data types such as Text and Document and labeling workflows including Evaluation, Rating, and Text Generation.

Labeling Experience

AI Technical Content Evaluator & Subject Matter Expert - Technical Content Optimization Platforms

TextRLHF

You evaluated and audited AI-generated technical content by applying prompt-based evaluation parameters and rigorous quality metrics. You reviewed complex STEM explanations and solutions to verify factual clarity, formatting compliance, and step-by-step structural logic. You calibrated instructional difficulty and generated actionable RLHF feedback by identifying edge cases and logical gaps in outputs. • Managed prompt-based evaluation workflows and applied quality metrics for LLM training support • Verified multi-variable STEM reasoning for factual accuracy and formatting compliance • Audited and calibrated evaluation framework difficulty and safety guardrails • Identified edge cases, logical gaps, and produced domain rule-based RLHF feedback

2025 - Present

Technical Subject Matter Expert & Writer

Don't discloseTextText Generation

Authored and assessed complex multi-variable technical and mathematical problems intended for AI tutoring, training, and evaluation use. Wrote highly structured instructional reports that translate intricate regulatory, scientific, and technical calculations into clear learning-ready formats with precise citations. Designed multi-layered scenarios with deliberate hidden variables to probe reasoning limits and verify mathematical correctness and formatting fidelity across generated solutions. • Formulated and solved problems with complex logical constraints and empirical methodologies • Evaluated multi-step answers for correctness and structural logic • Ensured rigid formatting consistency for instructional delivery • Built adversarial scenarios to test automated logical deductions

2025 - Present

AI Technical Content Evaluator & Subject Matter Expert

Don't discloseText

Evaluated and audited AI-generated technical explanations and multi-variable STEM solutions for factual clarity, formatting compliance, and step-by-step structural logic. Calibrated conceptual difficulty and instructional value of advanced evaluation frameworks and safety guardrails, and identified edge cases and logical gaps to translate domain rules into actionable RLHF feedback signals. Performed prompt-based evaluation parameter management and quality metric application to support training of state-of-the-art LLMs. • Reviewed logical steps and mathematical reasoning for absolute accuracy • Verified rigid formatting consistency and Markdown/LaTeX presentation requirements • Produced/triaged RLHF-style feedback derived from detected rule violations • Graded and benchmarked responses using quality metrics and evaluation frameworks

2025 - Present

Laboratory Researcher & Analytical Auditor

Don't discloseDocument

Conducted analytical auditing of civil engineering laboratory research outputs and peer datasets to isolate and eliminate formatting discrepancies, logical fallacies, and calculation errors. Interpreted experimental data and built predictive metrics while assessing safety and risk conditions using standardized engineering compliance rules. Cross-referenced and validated technical reports to ensure alignment with rigid engineering frameworks and documentation expectations. • Verified stress-strain properties, matrix calculations, and fluid drainage constraint results • Built predictive metrics from experimental data points • Audited cross-document consistency and compliance with engineering standards • Identified and corrected errors in technical reporting and peer datasets

2024 - 2025

Education

Work History