Mike P.

Key Skills

Software

Mercor

Scale AI

Top Subject Matter

Frontend engineering evaluation for LLM outputs — HTML, CSS, JavaScript, React, Tailwind, responsive layouts, and UI implementation quality

UX/UI evaluation for LLM outputs — design QA, rubric scoring, usability review, multimodal screen analysis, and interface critique

Product design, web app QA, and usability review — SaaS dashboards, e-commerce platforms, admin panels, mobile app flows, and design-system consistency

Top Data Types

Document

Computer Code Programming

Image

Top Task Types

Computer Programming/Coding

Evaluation/Rating

Classification

Freelancer Overview

Backend Code Evaluation – LLM Training Contributor (Mercor Platform). Brings 13+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Mercor. Education includes N/A, Boston University and N/A, Berry College. AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Frontend & UX/UI Evaluation – Design QA & Multimodal Review Contributor (Mercor Platform)

MercorText

Evaluated AI-generated HTML, CSS, and JavaScript outputs against design specifications to detect layout regressions, accessibility gaps, and responsiveness issues. Performed design-to-implementation QA by comparing component-level visual fidelity, information hierarchy, and interaction patterns with reference mockups and UX heuristics. Applied structured rubrics to assess LLM-generated UX copy, interface flows, and wireframe descriptions for clarity, usability, and alignment with product intent. • Annotated multimodal outputs combining visual and textual reasoning about UI screens • Reviewed frontend maintainability, semantic HTML, and cross-browser compatibility • Produced actionable feedback to improve generated UI behavior and documentation • Bridged backend perspectives with user-facing implementation standards

2024 - Present

Backend Code Evaluation – LLM Training Contributor (Mercor Platform)

Mercor

Reviewed and annotated AI-generated Java and Python code to assess correctness, idiomatic style, security, and performance in backend engineering contexts. Evaluated LLM prompt-response pairs by identifying hallucinations, anti-patterns, and unsafe practices to improve model quality. Produced high-quality reference implementations and ideal responses for training data covering Spring Boot, microservices, Kafka, and cloud infrastructure topics. • Applied RLHF preference rankings on comparative model outputs • Focused on REST API design, database access, and distributed systems patterns • Flagged issues affecting reliability, maintainability, and efficiency • Provided training feedback aligned with backend correctness scoring

2024 - Present

Education

Work History