Bill Malea

Key Skills

Software

Mercor

AWS SageMaker

Other

Top Subject Matter

Engineering (Software, Electrical, Chemical)

Software Engineering (AI/LLM)

AI Systems Evaluation

Top Data Types

Text

Computer Code Programming

Document

Top Task Types

Prompt Response Writing SFT

Evaluation Rating

Computer Programming Coding

Function Calling

RLHF

Freelancer Overview

Engineering Expert Reviewer (Multi‑Disciplinary). Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Mercor, Internal, and Proprietary Tooling. Education includes Master of Science, University College London (2026) and Bachelor of Science, University of Eldoret (2020). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Code Quality Software Engineer Reviewer

Mercor

Reviewed production-level software code and associated outputs as training data for AI reasoning models. Labeled, corrected, and improved step-by-step outputs to strengthen model reliability and reduce hallucinations. Applied engineering expertise to align training data with authentic real-world system behavior. • Evaluated model-generated reasoning traces for validity • Enhanced data completeness and logical consistency • Improved engineering outcome alignment through targeted edits • Ensured high-quality LLM code training samples

Reviewed production-level software code and associated outputs as training data for AI reasoning models. Labeled, corrected, and improved step-by-step outputs to strengthen model reliability and reduce hallucinations. Applied engineering expertise to align training data with authentic real-world system behavior. • Evaluated model-generated reasoning traces for validity • Enhanced data completeness and logical consistency • Improved engineering outcome alignment through targeted edits • Ensured high-quality LLM code training samples

2026 - 2026

Engineering Expert Reviewer (Multi‑Disciplinary)

Mercor

Reviewed and evaluated technical submissions for inclusion in AI training pipelines. Applied standardized criteria to assess the quality and applicability of engineering solutions for large-scale machine learning training. Ensured data met rigorous requirements before incorporation into model development workflows. • Validated logical and methodological soundness across multidisciplinary engineering data • Detected ambiguities and flagged unsafe assumptions impacting model accuracy • Provided detailed judgment beyond mere code review • Contributed to error reduction in AI output through refined labeling practices

Reviewed and evaluated technical submissions for inclusion in AI training pipelines. Applied standardized criteria to assess the quality and applicability of engineering solutions for large-scale machine learning training. Ensured data met rigorous requirements before incorporation into model development workflows. • Validated logical and methodological soundness across multidisciplinary engineering data • Detected ambiguities and flagged unsafe assumptions impacting model accuracy • Provided detailed judgment beyond mere code review • Contributed to error reduction in AI output through refined labeling practices

2026 - 2026

Software Engineer (AI Systems & Evaluation)

Text

Designed tasks and evaluation protocols to benchmark AI model quality. Analyzed and tested model responses for consistency, failure modes, and output reliability. Contributed to systematic process improvements for large-scale AI model evaluation. • Developed structured edge-case scenarios • Ran systematic quality assurance and benchmarking • Provided actionable feedback for reliability improvement • Applied multidomain engineering expertise to output assessment

Designed tasks and evaluation protocols to benchmark AI model quality. Analyzed and tested model responses for consistency, failure modes, and output reliability. Contributed to systematic process improvements for large-scale AI model evaluation. • Developed structured edge-case scenarios • Ran systematic quality assurance and benchmarking • Provided actionable feedback for reliability improvement • Applied multidomain engineering expertise to output assessment

2024 - 2025

Education

Work History