Joseph K.

Key Skills

Software

Clickworker

Data Annotation Tech

CrowdSource

Labelbox

Mercor

Remotasks

Telus

Other

Internal/Proprietary Tooling

Top Subject Matter

Cognitive Science

Psychology Domain Expertise

Computer Science

Top Data Types

Audio

Video

Document

Top Task Types

RLHF

Text Generation

Question Answering

Bounding Box

Classification

Object Detection

Text Summarization

Fine-tuning

Transcription

Data Collection

Evaluation/Rating

Freelancer Overview

Workspace Environment Agent — AfterQuery Experts (RLHF evaluation and technical data annotation). Brings around a year of professional experience across complex professional workflows, research, and quality-focused execution. AI-training focus includes data types such as Text and labeling workflows including RLHF, Text Generation, and Question Answering. Education: Bachelor of Cognitive Science, University of California, Merced (2028) High School Diploma, John Adams Academy (2024)

Labeling Experience

AI Research Scientist — Outlier (prompt + evaluation + anomaly detection support)

TextQuestion Answering

Contributed human expertise to train and refine AI models through prompt writing and evaluation-centric labeling tasks. Identified data anomalies and created problem/answer pairs to support supervised training and model improvement. Evaluated AI outputs (including code and text) to flag issues and guide refinement of model responses. • Wrote prompts and created problem/answer pairs for training • Evaluated AI outputs (code and text) and assessed quality • Identified anomalies in data to improve training robustness • Produced human-labeled examples to support ongoing model refinement

2026 - Present

Generative AI Engineer — Handshake (data annotation + AI output evaluation)

TextText Generation

Labeled and categorized data to improve Large Language Model training in specialized domains. Evaluated AI-generated text for accuracy, reasoning, and overall quality as part of an iterative model improvement workflow. Developed and tested prompts to probe model limits and support better model capability within targeted subject areas. • Labeled and categorized training/evaluation data for domain specialization • Evaluated AI outputs (text) for accuracy, reasoning, and quality • Performed prompt engineering to test and extend model behavior • Collaborated with subject-matter experts to enhance domain performance

2026 - Present

Workspace Environment Agent — AfterQuery Experts (RLHF evaluation and technical data annotation)

TextRLHF

Evaluated and refined Large Language Model outputs to align with human intent and academic/technical standards. Performed RLHF-oriented review of generated responses and contributed technical data annotation to improve model behavior. Focused on reliability and correctness for domain-specific tasks, including cognitive science, psychology, and computer science. • Reviewed LLM responses for accuracy, reasoning quality, and alignment with intent • Applied RLHF evaluation and refinement processes to guide improvements • Conducted technical data annotation to support training/evaluation • Ensured outputs met high-level academic and technical benchmarks

2026 - Present

Education

Work History