Aries Lusk

Key Skills

Software

Datasaur

Labelbox

LightTag

Remotasks

Scale AI

SuperAnnotate

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Label Types

Entity Ner Classification

Evaluation Rating

Fine Tuning

Prompt Response Writing SFT

Text Summarization

Freelancer Overview

I am an experienced AI Data Specialist with a proven record training and evaluating large language models (LLMs) and generative AI systems for leading firms including RWS and Innodata. My work spans data labeling, annotation, and model evaluation across NLP, computer vision, and multimodal datasets. I specialize in text classification, summarization, prompt engineering, content evaluation, and reinforcement learning from human feedback (RLHF) to improve AI alignment, accuracy, and contextual understanding. At RWS, I contributed to projects refining generative model outputs for coherence, factual precision, and tone alignment, while at Innodata I supported LLM fine-tuning and dataset curation for enterprise AI clients. I have hands-on experience with tools such as Labelbox, SageMaker Ground Truth, Scale AI, and custom annotation platforms, ensuring high-quality labeled data that drives better model performance. Holding a Bachelor’s in Computer Science and certifications in Generative AI and Machine Learning, I combine technical rigor with creative problem-solving. My passion lies in advancing AI systems that are not only intelligent but human-aligned, ethical, and globally scalable.

Labeling Experience

NLP Dataset Annotation for Generative Language Models

LabelboxTextEntity Ner Classification

Annotated and curated text datasets for large language model (LLM) fine-tuning. Performed entity recognition, classification, and summarization tasks to enhance model understanding and output quality. Developed prompt + response pairs for supervised fine-tuning (SFT) and evaluated model responses to ensure alignment with quality standards and desired tone. Managed dataset size of over 50,000+ text entries, ensuring high accuracy and consistency. The project improved generative AI capabilities in producing creative marketing copy, editorial content, and client-facing NLP outputs.

Annotated and curated text datasets for large language model (LLM) fine-tuning. Performed entity recognition, classification, and summarization tasks to enhance model understanding and output quality. Developed prompt + response pairs for supervised fine-tuning (SFT) and evaluated model responses to ensure alignment with quality standards and desired tone. Managed dataset size of over 50,000+ text entries, ensuring high accuracy and consistency. The project improved generative AI capabilities in producing creative marketing copy, editorial content, and client-facing NLP outputs.

2024

Generative AI Dataset Curation & Fine-Tuning

Scale AIImageText GenerationPrompt Response Writing SFT

Curated, annotated, and fine-tuned large-scale datasets for generative AI applications, including image, video, text, and synthetic content. Performed bounding box, polygon, and segmentation labeling on visual datasets for high-quality output generation. Conducted entity recognition, classification, and text generation labeling for NLP datasets, and executed prompt + response writing for supervised fine-tuning (SFT) of generative language models. Oversaw dataset quality control to ensure model accuracy and consistency. Project involved iterative testing, dataset augmentation, and evaluation to optimize model performance for editorial, animation, and marketing content.

Curated, annotated, and fine-tuned large-scale datasets for generative AI applications, including image, video, text, and synthetic content. Performed bounding box, polygon, and segmentation labeling on visual datasets for high-quality output generation. Conducted entity recognition, classification, and text generation labeling for NLP datasets, and executed prompt + response writing for supervised fine-tuning (SFT) of generative language models. Oversaw dataset quality control to ensure model accuracy and consistency. Project involved iterative testing, dataset augmentation, and evaluation to optimize model performance for editorial, animation, and marketing content.

2024

Visual Dataset Labeling & Synthetic Data Generation for Generative AI

LabelboxVideoFine Tuning

Led a comprehensive dataset labeling project for generative AI models, focused on visual content for editorial and animation purposes. Performed bounding box, polygon, segmentation, and object tracking on 20,000+ images and 500+ video clips. Created synthetic datasets to augment training data, ensuring models produced high-fidelity outputs aligned with artistic and creative standards. Conducted quality control, fine-tuning, and evaluation to optimize model performance. This project successfully enabled generative AI models to produce professional-grade creative visuals for media and marketing campaigns.

Led a comprehensive dataset labeling project for generative AI models, focused on visual content for editorial and animation purposes. Performed bounding box, polygon, segmentation, and object tracking on 20,000+ images and 500+ video clips. Created synthetic datasets to augment training data, ensuring models produced high-fidelity outputs aligned with artistic and creative standards. Conducted quality control, fine-tuning, and evaluation to optimize model performance. This project successfully enabled generative AI models to produce professional-grade creative visuals for media and marketing campaigns.

2025 - 2025

Education

Work History