Isaac Kim

Key Skills

Software

AWS SageMaker

Appen

Data Annotation Tech

Labelbox

Mindrift

OneForma

Remotasks

Scale AI

Toloka

Telus

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Geospatial Tiled Imagery

Image

Top Task Types

Classification

Computer Programming Coding

Data Collection

RLHF

Text Summarization

Freelancer Overview

I am an experienced AI data labeler and training contributor with a strong track record on platforms such as Outlier AI, Mindrift, and Remotask. Over the past few years, I have participated in a wide range of annotation projects supporting the development of cutting-edge AI systems across domains like natural language processing (NLP), computer vision (CV), and large language model (LLM) evaluation. My contributions have included prompt rating, dialogue comparison, text classification, sentiment analysis, object detection, and video frame labeling, all completed with a high degree of precision and consistency. I excel in following detailed guidelines, maintaining strict quality standards, and adapting quickly to new task types or toolsets. My ability to assess linguistic, contextual, and visual content has helped refine and align AI outputs for improved real-world performance. With strong analytical thinking, deep attention to detail, and an appreciation for both human and machine learning systems, I bring a versatile and reliable presence to any data training team.

Labeling Experience

Text Evaluation and Prompt Rating for Large Language Models

Scale AITextEntity Ner ClassificationText Generation

Worked on diverse LLM evaluation projects including rating chatbot responses, comparing answer quality, classifying tone and appropriateness, and rewriting prompts for model fine-tuning. I also performed Named Entity Recognition (NER), summarization, and content moderation tasks. My annotations helped train safer, more coherent language models, and I was consistently selected for high-trust tasks like alignment tuning and red teaming.

Worked on diverse LLM evaluation projects including rating chatbot responses, comparing answer quality, classifying tone and appropriateness, and rewriting prompts for model fine-tuning. I also performed Named Entity Recognition (NER), summarization, and content moderation tasks. My annotations helped train safer, more coherent language models, and I was consistently selected for high-trust tasks like alignment tuning and red teaming.

2023

Image Annotation for Object Detection and E-commerce Tagging

Scale AIImageBounding BoxClassification

I worked on image labeling projects involving precise bounding box annotations for objects in various environments, including street scenes (self-driving car training) and product images for e-commerce platforms. Tasks included tagging fashion and consumer goods for retail datasets, annotating safety-critical signs, vehicles, pedestrians, and road objects. I maintained consistent accuracy above 95% and ensured clear object class differentiation, even in cluttered environments.

I worked on image labeling projects involving precise bounding box annotations for objects in various environments, including street scenes (self-driving car training) and product images for e-commerce platforms. Tasks included tagging fashion and consumer goods for retail datasets, annotating safety-critical signs, vehicles, pedestrians, and road objects. I maintained consistent accuracy above 95% and ensured clear object class differentiation, even in cluttered environments.

2022 - 2024

Video Annotation for Object Tracking and Action Recognition

CVATVideoBounding BoxPolyline

Contributed to video annotation projects that required tracking multiple moving objects across sequential frames. Tasks included labeling vehicle movements, tracking pedestrian activity, drawing polylines for lane boundaries, and identifying specific behaviors (e.g., jaywalking, abrupt stops). I applied temporal consistency checks and verified annotations using reference frame guidelines. The project supported training object-tracking algorithms for real-time applications.

Contributed to video annotation projects that required tracking multiple moving objects across sequential frames. Tasks included labeling vehicle movements, tracking pedestrian activity, drawing polylines for lane boundaries, and identifying specific behaviors (e.g., jaywalking, abrupt stops). I applied temporal consistency checks and verified annotations using reference frame guidelines. The project supported training object-tracking algorithms for real-time applications.

2021 - 2022

Education

Work History