Hudson G.

Key Skills

Software

Other

Don't disclose

Top Subject Matter

Large language model (LLM) output evaluation and RLHF

Computer vision dataset annotation (semantic segmentation)

Dataset operations and quality monitoring via databases

Top Data Types

Text

Image

Top Task Types

RLHF

Segmentation

Data Collection

Freelancer Overview

AI Training Specialist & Data Annotator (Remote) — Model Optimization & RLHF. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Science, University of California, Los Angeles (UCLA) (2022) and High School Diploma, Lowell High School (2018). AI-training focus includes data types such as Text and Image and labeling workflows including RLHF, Segmentation, and Data Collection.

Labeling Experience

Data Entry & Operations Management Specialist — Database Maintenance

Don't discloseTextData Collection

Managed database-related data operations by tracking complex datasets, project progress metrics, and performance scores. Entered information into secure online databases for real-time quality monitoring. Focused on accurate data entry and validation to support ongoing dataset and project oversight. • Logged dataset and progress metrics for monitoring • Inputted records into secure online databases • Validated data entries for accuracy and completeness • Supported quality assurance through continuous tracking

2025 - Present

AI Training Specialist & Data Annotator (Remote) — Image Labeling & Computer Vision

OtherImageSegmentation

Performed high-fidelity image annotation including bounding box creation and semantic segmentation to optimize training datasets. Maintained strict data integrity standards to ensure annotations were accurate and suitable for autonomous and robotic vision system training. Contributed to dataset quality improvements by correcting and validating thousands of annotated items. • Created bounding boxes and semantic segmentation masks • Followed detailed annotation guidelines for consistency • Conducted quality checks to minimize label errors • Prepared labeled data for machine learning ingestion

2025 - Present

AI Training Specialist & Data Annotator (Remote) — Model Optimization & RLHF

TextRLHF

Evaluated and graded AI-generated outputs for factual accuracy, structural logic, and linguistic coherence using specialized training guidelines. This work supported RLHF-style improvement loops for advanced large language models by identifying errors and inconsistencies. Ensured outputs met quality and safety expectations for model ingestion and downstream use. • Reviewed model responses against rubric-based criteria • Assessed reasoning quality, coherence, and compliance risks • Provided performance signals to guide model optimization • Verified consistency with training instructions and constraints

2025 - Present

Education

Work History