David Carter

Key Skills

Software

Remotasks

Appen

Labelbox

Top Subject Matter

Legal Services & Contract Review

Medical Domain Expertise

Technical Domain Expertise

Top Data Types

Text

Computer Code Programming

Video

Top Task Types

Entity Ner Classification

Bounding Box

Point Key Point

Classification

Text Generation

Question Answering

Text Summarization

RLHF

Transcription

Evaluation Rating

Computer Programming Coding

Fine Tuning

Function Calling

Data Collection

Prompt Response Writing SFT

Red Teaming

Freelancer Overview

Senior AI Output Evaluator & Content Rater. Brings 4+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Remotasks, Appen, and Labelbox. Education includes Bachelor of Science, N/A (2024) and Certificate, University of Pennsylvania (Wharton School) (2023). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

Senior AI Output Evaluator & Content Rater

RemotasksText

In this role, I reviewed and evaluated high volumes of AI-generated text outputs across multiple domains. My work involved annotating, rating, and flagging text data for model improvement using multi-step rubrics. I consistently maintained superior adherence scores and provided structured feedback to enhance RLHF training processes. • Maintained a 99.1% guideline adherence rate across 24+ months and processed over 12,000–15,000 outputs monthly. • Annotated 80,000+ data points from legal, medical, technical, and creative datasets while identifying 3,200+ edge cases for rubric improvement. • Delivered detailed feedback on low-quality outputs and escalated ambiguous guideline instances to reduce team calibration errors. • Upheld throughput of 500+ evaluated tasks per day during sustained 6–8 hour independent work sessions.

In this role, I reviewed and evaluated high volumes of AI-generated text outputs across multiple domains. My work involved annotating, rating, and flagging text data for model improvement using multi-step rubrics. I consistently maintained superior adherence scores and provided structured feedback to enhance RLHF training processes. • Maintained a 99.1% guideline adherence rate across 24+ months and processed over 12,000–15,000 outputs monthly. • Annotated 80,000+ data points from legal, medical, technical, and creative datasets while identifying 3,200+ edge cases for rubric improvement. • Delivered detailed feedback on low-quality outputs and escalated ambiguous guideline instances to reduce team calibration errors. • Upheld throughput of 500+ evaluated tasks per day during sustained 6–8 hour independent work sessions.

2022 - Present

Content-Based Rater & Quality Analyst

AppenText

As a Content-Based Rater and Quality Analyst, I executed content rating and relevance annotation for diverse datasets, including web search, images, and conversational AI interactions. I applied structured scoring rubrics for multi-turn dialogue and conducted quality audits for peer annotation tasks. I worked with detailed project guidelines, contributed to guideline updates, and maintained high project accuracy scores. • Completed over 200,000 annotations with a project-average accuracy of 98.3% across varied data types. • Processed 18,000+ multi-turn dialogue pairs evaluating for safety, relevance, and response quality. • Audited 1,500+ peer tasks weekly, flagging errors for batch-level quality improvement. • Authored formal feedback and maintained eligibility for specialist task pipelines.

As a Content-Based Rater and Quality Analyst, I executed content rating and relevance annotation for diverse datasets, including web search, images, and conversational AI interactions. I applied structured scoring rubrics for multi-turn dialogue and conducted quality audits for peer annotation tasks. I worked with detailed project guidelines, contributed to guideline updates, and maintained high project accuracy scores. • Completed over 200,000 annotations with a project-average accuracy of 98.3% across varied data types. • Processed 18,000+ multi-turn dialogue pairs evaluating for safety, relevance, and response quality. • Audited 1,500+ peer tasks weekly, flagging errors for batch-level quality improvement. • Authored formal feedback and maintained eligibility for specialist task pipelines.

2020 - 2022

Data Annotation Specialist (Freelance)

LabelboxTextEntity Ner Classification

As a freelance data annotation specialist, I delivered foundational AI training data for classification, NER, sentiment, and text span labeling tasks. My responsibilities included applying fine-grained taxonomies and managing high-volume annotation batches independently. I maintained strong accuracy and self-audited output quality for each contract engagement. • Annotated 30,000+ text items with verified 97.8% accuracy, including implicit bias, toxicity, and intent classification. • Applied classification guidelines for NER and sentiment analysis using Labelbox and Outlier AI platforms. • Executed batches averaging 400+ annotated items per day while balancing workload autonomously. • Managed multiple tasks for clients in AI development and research settings.

As a freelance data annotation specialist, I delivered foundational AI training data for classification, NER, sentiment, and text span labeling tasks. My responsibilities included applying fine-grained taxonomies and managing high-volume annotation batches independently. I maintained strong accuracy and self-audited output quality for each contract engagement. • Annotated 30,000+ text items with verified 97.8% accuracy, including implicit bias, toxicity, and intent classification. • Applied classification guidelines for NER and sentiment analysis using Labelbox and Outlier AI platforms. • Executed batches averaging 400+ annotated items per day while balancing workload autonomously. • Managed multiple tasks for clients in AI development and research settings.

2020 - 2020

Education

Work History