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Dennis M.

Dennis M.

AI Data Annotation Specialist · RLHF & NLP · Text, Image & Multimodal Labelling

USA flagBloomington, Usa

Key Skills

Software

No software listed

Top Subject Matter

My strongest subject matter areas for AI training
evaluation are data annotation
RLHF (Reinforcement Learning from Human Feedback)
NLP tasks. I have hands-on experience with text evaluation
comparative analysis of language model outputs

Top Data Types

ImageImage
VideoVideo
TextText

Top Task Types

Entity (NER) ClassificationEntity (NER) Classification
RelationshipRelationship
Question AnsweringQuestion Answering
Text GenerationText Generation
Action RecognitionAction Recognition
Emotion RecognitionEmotion Recognition

Freelancer Overview

Over the past several months, I've actively engaged with multiple AI evaluation and data annotation platforms, including Outlier AI, Prolific, Mindrift, and others. This work has exposed me to diverse task types—from evaluating AI-generated content and assessing reasoning quality, to providing comparative judgments between model outputs and flagging content that violates guidelines. I've developed strong familiarity with platform workflows, instruction following, and maintaining consistency across large batches of assessments. My ability to rotate between different task categories and subject domains has sharpened my capacity to think critically across varied contexts while adhering to precise evaluation criteria. This gig-based experience has reinforced several core competencies: meticulous attention to detail, the ability to break down complex instructions and apply them consistently, comfort with ambiguity and nuanced decision-making, and self-direction in meeting quality standards without direct oversight. I've also navigated the administrative side of multiple platforms—onboarding, qualification assessments, payment processing—which has given me insight into how these ecosystems operate. I'm drawn to AI evaluation as a discipline because it sits at the intersection of careful analysis, clear communication, and the meaningful work of helping AI systems become more reliable and aligned with human value

Labeling Experience

I have active, hands-on experience across multiple AI evaluation and data annotation platforms, including Outlier AI, Pr

I have active, hands-on experience across multiple AI evaluation and data annotation platforms, including Outlier AI, Prolific, Mindrift, Toloka, and Mercor. Over the past several months, I've completed diverse assessment types—from content quality evaluation and comparative analysis of AI outputs, to instruction adherence checks and guideline-based flagging. This work has given me direct exposure to the full spectrum of evaluation workflows: understanding complex task instructions, maintaining consistency across large batches, making nuanced judgments in ambiguous scenarios, and documenting my reasoning clearly. I've successfully navigated qualification assessments on multiple platforms and consistently met quality standards required to remain in good standing. Beyond the evaluation work itself, I've gained insight into the operational and administrative side of these platforms—onboarding processes, payment systems, task routing, and how to troubleshoot issues independently. This experience has reinforced my ability to work with precision and self-direction: following detailed specifications exactly, managing my own quality control, and communicating clearly when ambiguities arise. I'm drawn to this work because it combines critical analysis with real impact—contributing to datasets that help train and improve AI systems. I understand what excellence looks like in this space and am committed to delivering consistent, thoughtful evaluation work.

Not specified

Education

B

Bachelor Of Arts(Political Science) Bachelor Of Science(Computer Science) Principia College

Degree not specified

Not specified
Not specified

Work History

C

Company not specified

AI Data Annotator — NLP & Content Review | DataAnnotation.techDec 2023 – Jan 2026 · Remote, US • Reviewed and evaluated

Location not specified
2023 - 2026