Dorian M.

Key Skills

Software

Scale AI

Appen

Labelbox

CrowdFlower

CloudFactory

CrowdSource

Datatroniq

Deep Systems

Doccano

Hasty

HiveMind

Kili Technology

LightTag

Top Subject Matter

Nlp Domain Expertise

multimodal AI

legal/medical/conversational AI

Top Data Types

Text

Image

Document

Top Task Types

Entity (NER) Classification

Classification

Cuboid

Question Answering

RLHF

Fine-tuning

Transcription

Evaluation/Rating

Data Collection

Function Calling

Freelancer Overview

Senior Data Annotation Specialist & AI Trainer (Outlier AI / Scale AI (Remotasks)). Brings 5+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI, Appen, and Labelbox. Education includes Bachelor of Science, Tempe High School (2024). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification, Evaluation, and Rating.

Labeling Experience

Senior Data Annotation Specialist & AI Trainer (Outlier AI / Scale AI (Remotasks))

Scale AITextEntity (NER) Classification

Performed named entity recognition (NER) tagging, intent classification, sentiment labeling, and text span annotation for NLP and multimodal AI datasets. Applied rubric-based evaluation to rate AI-generated outputs for instruction-following, factual accuracy, coherence, and safety compliance to support RLHF training batches. Maintained high accuracy through blind quality-check audits and peer review. • Labeled conversational and legal/medical text corpora • Escalated 800+ edge cases and ambiguous instances to improve guidelines • Audited 1,200+ weekly submissions as a peer-review auditor • Achieved 98.6% accuracy and 96.8% inter-rater agreement on relevant tasks

2023 - 2023

Data Labeling & Content Rating Analyst (Appen)

AppenText

Completed high-volume content rating and relevance annotation tasks across web search, image, and conversational AI datasets. Evaluated multi-turn conversational AI outputs for safety, relevance, tone, and response quality using structured scoring rubrics. Followed and complied with lengthy project style guides and annotation standards validated through blind quality audits. • Completed 150,000+ individual annotations with 97.9% project-average accuracy • Scored 12,000+ dialogue pairs over an 8-month engagement • Built ontologies and classification taxonomies for nuanced content categories • Maintained quality score of 4.7/5.0 and earned early access to high-tier tasks

2022 - 2023

AI & Data Annotation Intern (Arizona State University)

LabelboxText

Performed high-volume text and image annotation for NLP and computer vision research projects, maintaining 97%+ accuracy. Applied NER tagging, sentiment classification, and bounding box and polygon annotations using Labelbox and CVAT. Developed and refined annotation rubrics and performed inter-rater agreement evaluations to support RLHF data preparation and model training. • Annotated 15,000+ data points across text and vision modalities • Used Labelbox and CVAT for structured annotation workflows • Conducted inter-rater agreement evaluations for annotation consistency • Supported rubric refinement and RLHF-oriented dataset preparation

2022 - 2022

Education

Work History