Benjamin H.

Key Skills

Software

Data Annotation Tech

OneForma

Remotasks

Scale AI

Top Subject Matter

Large language model training data labeling and quality assurance

LLM prompt evaluation and feedback collection for RLHF

Top Data Types

Text

Image

Video

Top Task Types

RLHF

Prompt + Response Writing (SFT)

Freelancer Overview

Freelance Software Developer & AI Contractor (AI training pipelines, annotation frameworks, QA workflows). Brings 22+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master of Science, Eastern University (2015) and Bachelor of Science, Eastern University (2005). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including RLHF and Prompt + Response Writing (SFT).

Labeling Experience

LLM Prompt Evaluation Tool (internal data collection for RLHF)

Prompt + Response Writing (SFT)

Developed an internal LLM prompt evaluation workflow to enable non-technical team members to assess and flag model responses. Collected evaluation outputs and fed them back into an RLHF loop to support iterative model improvement. Enabled structured rating/flagging during prompt-response testing through a web-based interface. • Created an internal web tool for model response evaluation by team members. • Structured evaluation/flagging outputs for downstream RLHF feedback. • Supported non-technical evaluators with usable UI and guidance. • Integrated evaluation results into the iterative training workflow.

2019 - Present

Freelance Software Developer & AI Contractor (AI training pipelines, annotation frameworks, QA workflows)

TextRLHF

Provided support for large language model training pipelines by working on data annotation frameworks and quality assurance workflows for AI-focused clients. Focused on preparing and validating conversational training data to improve downstream model behavior in RLHF-style loops. Delivered tooling and documentation to ensure consistent label quality across distributed contributors. • Built/maintained training-pipeline components supporting annotation and QA workflows. • Reduced manual annotation time by ~40% via automated data processing tools. • Designed REST APIs to support internal annotation tools and third-party integrations. • Collaborated with distributed AI teams to align labeling standards and workflows.

2019 - Present

Education

Work History