Stevie Dolman

Key Skills

Software

Telus

Appen

Clickworker

Data Annotation Tech

Labelbox

Mercor

Micro1

Mindrift

OneForma

Remotasks

Scale AI

Toloka

Top Subject Matter

Large Language Models (LLMs)

AI Safety

RLHF / Model Evaluation

Top Data Types

Text

Image

Document

Top Task Types

Classification

Object Detection

Text Generation

Question Answering

Text Summarization

RLHF

Red Teaming

Transcription

Evaluation/Rating

Data Collection

Prompt + Response Writing (SFT)

Bounding Box

Freelancer Overview

AI Data Trainer & Evaluator – Outlier. Brings 11+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Outlier, Telus, and Atlas Capture. Education includes Multiple AI Platforms, AI Data Trainer & Evaluator (2024) and 2 Man Home Delivery, Logistics Coordinator (2025). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

RLHF / LLM evaluation

TextRLHF

Worked on RLHF-based training and evaluation of large language models, focusing on assessing and improving model outputs for accuracy, safety, and instruction adherence. Tasks included ranking multiple responses, identifying hallucinations, evaluating factual correctness, tone, and policy compliance, and providing structured feedback to guide model optimisation. Applied detailed annotation guidelines and quality standards to ensure consistency across evaluations, contributing to the refinement of AI behaviour and user-aligned responses. Experience includes handling edge cases, ambiguous prompts, and nuanced judgment tasks requiring critical thinking and contextual understanding.

Worked on RLHF-based training and evaluation of large language models, focusing on assessing and improving model outputs for accuracy, safety, and instruction adherence. Tasks included ranking multiple responses, identifying hallucinations, evaluating factual correctness, tone, and policy compliance, and providing structured feedback to guide model optimisation. Applied detailed annotation guidelines and quality standards to ensure consistency across evaluations, contributing to the refinement of AI behaviour and user-aligned responses. Experience includes handling edge cases, ambiguous prompts, and nuanced judgment tasks requiring critical thinking and contextual understanding.

2026 - Present

AI Evaluation Contributor – Uber AI

Text

As an AI Evaluation Contributor for Uber AI, I engaged in structured workflows to evaluate various AI model outputs. I provided actionable feedback for improving model performance and supporting effective training pipelines. The role relied upon effective workflow participation and quality-driven assessment skills. • Conducted structured evaluations of model predictions • Offered targeted feedback for system enhancement • Supported high-quality training pipelines • Ensured evaluation tasks followed prescribed guidelines.

As an AI Evaluation Contributor for Uber AI, I engaged in structured workflows to evaluate various AI model outputs. I provided actionable feedback for improving model performance and supporting effective training pipelines. The role relied upon effective workflow participation and quality-driven assessment skills. • Conducted structured evaluations of model predictions • Offered targeted feedback for system enhancement • Supported high-quality training pipelines • Ensured evaluation tasks followed prescribed guidelines.

Not specified

Data Labeling Specialist – Atlas Capture

ImageClassification

As a Data Labeling Specialist at Atlas Capture, I performed precise annotation of image and video datasets. I systematically flagged dataset defects and inconsistencies to maintain and enhance overall data quality. This work required a high level of accuracy and attention to detail under high-volume workloads. • Annotated both still images and video frames • Identified and flagged labeling inconsistencies • Maintained rigorous accuracy under tight deadlines • Helped build robust and reliable datasets.

As a Data Labeling Specialist at Atlas Capture, I performed precise annotation of image and video datasets. I systematically flagged dataset defects and inconsistencies to maintain and enhance overall data quality. This work required a high level of accuracy and attention to detail under high-volume workloads. • Annotated both still images and video frames • Identified and flagged labeling inconsistencies • Maintained rigorous accuracy under tight deadlines • Helped build robust and reliable datasets.

Not specified

Search Quality Rater – Telus Digital

TelusText

In my role as a Search Quality Rater with Telus Digital, I assessed the relevance, intent match, and usefulness of search outputs. I utilized complex guidelines designed to create high-quality training data for search models. My work demanded strong independent judgement and consistency to maintain data quality. • Evaluated search queries and output data • Applied detailed policy guidelines effectively • Generated high-quality search training data • Provided feedback for improved model performance.

In my role as a Search Quality Rater with Telus Digital, I assessed the relevance, intent match, and usefulness of search outputs. I utilized complex guidelines designed to create high-quality training data for search models. My work demanded strong independent judgement and consistency to maintain data quality. • Evaluated search queries and output data • Applied detailed policy guidelines effectively • Generated high-quality search training data • Provided feedback for improved model performance.

Not specified

AI Data Trainer & Evaluator – Outlier

Text

As an AI Data Trainer & Evaluator at Outlier, I evaluated and ranked large language model (LLM) responses for accuracy, helpfulness, and policy compliance. I applied detailed rubrics to ensure alignment with established safety and quality standards. My responsibilities included identifying edge cases and inconsistencies to enhance overall model robustness. • Evaluated LLM outputs across different platforms • Applied guidelines for response ranking and safety • Provided feedback for continuous improvement • Ensured high-quality, policy-aligned data generation.

As an AI Data Trainer & Evaluator at Outlier, I evaluated and ranked large language model (LLM) responses for accuracy, helpfulness, and policy compliance. I applied detailed rubrics to ensure alignment with established safety and quality standards. My responsibilities included identifying edge cases and inconsistencies to enhance overall model robustness. • Evaluated LLM outputs across different platforms • Applied guidelines for response ranking and safety • Provided feedback for continuous improvement • Ensured high-quality, policy-aligned data generation.

Not specified

Education

Work History