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Paul W.

Paul W.

AI Output Evaluator & Data Labeling Specialist

USA flagFairfax, Usa

Key Skills

Software

No software listed

Top Subject Matter

AI & Machine Learning
Creative & Media Production
Social Media & Digital Marketing
Music & Entertainment
Fashion & Luxury Brands

Top Data Types

ImageImage
VideoVideo
TextText

Top Task Types

Text GenerationText Generation
ClassificationClassification
Evaluation/RatingEvaluation/Rating
Object DetectionObject Detection
RLHFRLHF
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)

Freelancer Overview

Paul Watler II AI Trainer / Data Labeler Fairfax, VA · linkedin.com/in/paulwatlerii · github.com/xXDefcon-1Xx A B O U T I've spent the past couple years building AI-driven content systems from scratch — writing prompts, evaluating outputs, catching what went wrong, and figuring out how to fix it. Most of that work happened independently, which means I had to develop my own quality standards and enforce them consistently across hundreds of generated assets. I'm drawn to AI training work because it's essentially what I've already been doing: reading model outputs critically, documenting what works, and feeding that back into the process. E X P E R I E N C E AI Systems Builder / Prompt Engineer — Watler Studios — Independent 2024 – Present – Built and maintained multiple AI content pipelines across image and video models including Higgsfield, Kling, and Seedance — writing prompts, reviewing every output, and iterating based on what failed – Developed a locked 14-image reference system for an AI influencer project to keep identity, wardrobe, and visual details consistent across hundreds of assets — basically a manual annotation spec I built myself – Got good at spotting failure modes fast: underexposure, identity drift, anatomy issues, jewelry details rendering wrong — and rewriting prompts to address the actual root cause – Documented generation rules, platform-specific quirks, and model behaviors in structured notes — similar to annotation guidelines – One of the content pipeli

Labeling Experience

Yes, I have relevant experience

Yes, I have relevant experience. I've spent the past couple years independently building and evaluating AI-generated content pipelines — reviewing image, video, and text outputs against detailed specs, categorizing failure modes, and maintaining consistency standards across large batches of assets. The work is annotation-adjacent: I developed my own evaluation criteria, documented edge cases and decision rules, and iterated based on what the model got wrong. I'd describe my experience level as intermediate

Not specified

Education

S

School/Program: ZTM Academy (Zero to Mastery) Then for courses you can list whichever feel most relevant to the role: T

School/Program: ZTM Academy (Zero to Mastery) Then for courses you can list whichever feel most relevant to the role: The Vibe Coding Bootcamp: Become an AI-Augmented Developer — this is the most directly relevant one for a data labeling/AI training application Complete Cybersecurity Bootcamp: Zero to Mastery — decent to include, shows technical range Azure Bootcamp: Build AI Workflows & Agents — also relevant, shows you understand AI systems

School/Program: ZTM Academy (Zero to Mastery) Then for courses you can list whichever feel most relevant to the role: The Vibe Coding Bootcamp: Become an AI-Augmented Developer — this is the most directly relevant one for a data labeling/AI training application Complete Cybersecurity Bootcamp: Zero to Mastery — decent to include, shows technical range Azure Bootcamp: Build AI Workflows & Agents — also relevant, shows you understand AI systems
Not specified

Work History

C

Company not specified

Role: AI Output Evaluator Company: Watler Studios Duration: 2024 – Present

Location not specified
Not specified