Chloe Imbusch

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

Labelbox

OneForma

Remotasks

Toloka

Scale AI

Other

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Audio Recording

Classification

Computer Programming Coding

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I have hands-on experience in AI training and data annotation where I contributed to tasks such as evaluating AI-generated responses, refining prompt effectiveness, and ensuring content alignment with user intent. This role sharpened my attention to detail, consistency, and ability to assess the quality of natural language outputs across varied use cases. Alongside this experience, I bring a strong foundation in computer science and software development, with projects ranging from full-stack web apps to interactive educational games. My background in English further strengthens my ability to evaluate tone, clarity, and coherence—making me especially effective in tasks involving LLM evaluation, dialogue systems, and prompt optimization. I’m passionate about improving the human-AI interface through thoughtful, language-aware design and data quality.

Labeling Experience

Voice Data Collection & Quality Assurance

OtherAudioQuestion AnsweringAudio Recording

Participated in voice data collection and evaluation projects aimed at improving automatic speech recognition (ASR) systems. Tasks included recording scripted prompts, ensuring clear enunciation and pronunciation across diverse phrases, and verifying alignment between text and spoken content. This work contributed to enhancing multilingual voice datasets for use in training large-scale AI models, including virtual assistants and transcription systems. Demonstrated consistency, attention to detail, and high accuracy in audio-based annotation tasks.

Participated in voice data collection and evaluation projects aimed at improving automatic speech recognition (ASR) systems. Tasks included recording scripted prompts, ensuring clear enunciation and pronunciation across diverse phrases, and verifying alignment between text and spoken content. This work contributed to enhancing multilingual voice datasets for use in training large-scale AI models, including virtual assistants and transcription systems. Demonstrated consistency, attention to detail, and high accuracy in audio-based annotation tasks.

2024

Natural Language Evaluation & Prompt Optimization

Data Annotation TechTextText GenerationFine Tuning

Contributed to the evaluation and fine-tuning of large language models by ranking AI-generated responses, assessing prompt effectiveness, and flagging outputs for quality issues such as hallucinations, offensive content, or irrelevance. Tasks required a strong understanding of context, coherence, and tone, as well as precise judgment on language quality. Leveraged a background in English and Computer Science to evaluate linguistic accuracy while considering technical alignment with user prompts. This work directly supported reinforcement learning with human feedback (RLHF) workflows, improving LLM performance across conversational and task-based contexts.

Contributed to the evaluation and fine-tuning of large language models by ranking AI-generated responses, assessing prompt effectiveness, and flagging outputs for quality issues such as hallucinations, offensive content, or irrelevance. Tasks required a strong understanding of context, coherence, and tone, as well as precise judgment on language quality. Leveraged a background in English and Computer Science to evaluate linguistic accuracy while considering technical alignment with user prompts. This work directly supported reinforcement learning with human feedback (RLHF) workflows, improving LLM performance across conversational and task-based contexts.

2023 - 2024

Education

Work History