Bickham Trinity

Key Skills

Software

Appen

Clickworker

CloudFactory

Data Annotation Tech

HiveMind

Labelbox

OneForma

Scale AI

Snorkel AI

Surge AI

Toloka

Telus

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Data Collection

Evaluation Rating

Prompt Response Writing SFT

RLHF

Translation Localization

Freelancer Overview

I am an AI Training and Data Labeling Specialist with over 4 years of experience in curating, annotating, and evaluating high-quality datasets for machine learning and natural language processing models. My expertise spans image and video annotation, search relevance evaluation, metadata validation, and LLM output ranking, all performed with precision and a deep understanding of user intent. I have collaborated with major AI platforms on projects involving e-commerce search optimization, content moderation, and dataset quality assurance, ensuring consistency and accuracy across large-scale data pipelines. In addition to my technical background, I bring proficiency in annotation tools such as Labelbox, Scale AI, Appen, and Prodigy, as well as strong familiarity with prompt evaluation and RLHF techniques. My ability to work independently, meet tight deadlines, and adapt quickly to project guidelines sets me apart as a reliable contributor to AI training initiatives.

Labeling Experience

Image & Video Annotation for Computer Vision Models

SuperannotateImageBounding BoxSegmentation

Annotated and classified thousands of images and short video clips for computer vision model training, including drawing bounding boxes, object segmentation, and labeling object actions. Focused on ensuring precise object detection and scene understanding, with special attention to edge cases and visual anomalies. Conducted quality assurance checks to maintain over 97% accuracy across complex datasets.

Annotated and classified thousands of images and short video clips for computer vision model training, including drawing bounding boxes, object segmentation, and labeling object actions. Focused on ensuring precise object detection and scene understanding, with special attention to edge cases and visual anomalies. Conducted quality assurance checks to maintain over 97% accuracy across complex datasets.

2023

Text Summarization and Localization QA for AI Models

LabelboxTextText SummarizationTranslation Localization

Reviewed, summarized, and localized AI-generated text for global audiences to ensure accuracy, cultural relevance, and linguistic quality. Evaluated translations and summaries for tone, grammar, and adherence to project guidelines. Provided feedback to improve LLM performance on summarization and localization tasks.

Reviewed, summarized, and localized AI-generated text for global audiences to ensure accuracy, cultural relevance, and linguistic quality. Evaluated translations and summaries for tone, grammar, and adherence to project guidelines. Provided feedback to improve LLM performance on summarization and localization tasks.

2024 - 2024

Search Relevance and Ad Ranking Evaluation

AppenTextClassificationEvaluation Rating

Performed detailed evaluations of search engine results and sponsored ads to assess query relevance, user intent alignment, and ad quality. Scored and classified results to optimize search algorithms and improve user experience. Provided actionable feedback to enhance machine learning ranking models.

Performed detailed evaluations of search engine results and sponsored ads to assess query relevance, user intent alignment, and ad quality. Scored and classified results to optimize search algorithms and improve user experience. Provided actionable feedback to enhance machine learning ranking models.

2024 - 2024

LLM Output Evaluation and RLHF Annotation

Scale AITextText GenerationRLHF

Contributed to the optimization of large language models by evaluating and ranking AI-generated responses for accuracy, tone, factual correctness, and relevance. Tasks included prompt engineering, supervised fine-tuning (SFT), and reinforcement learning with human feedback (RLHF) to enhance the model’s conversational quality and reduce bias. Collaborated with QA teams to ensure alignment with strict annotation guidelines, achieving high consistency and precision across datasets.

Contributed to the optimization of large language models by evaluating and ranking AI-generated responses for accuracy, tone, factual correctness, and relevance. Tasks included prompt engineering, supervised fine-tuning (SFT), and reinforcement learning with human feedback (RLHF) to enhance the model’s conversational quality and reduce bias. Collaborated with QA teams to ensure alignment with strict annotation guidelines, achieving high consistency and precision across datasets.

2023 - 2024

Education

Work History