Samuel Lim

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

No data types listed

Top Label Types

No label types listed

Freelancer Overview

I am a detail-oriented data labeler with hands-on experience annotating text, image, audio, and video datasets for machine learning and AI model training. My expertise spans image and video annotation—including bounding boxes, polygon segmentation, and keypoint labeling—as well as text classification, sentiment analysis, and audio transcription. I am skilled in using annotation tools such as Labelbox, CVAT, V7, SuperAnnotate, and Doccano, and I consistently maintain high-quality standards with accuracy rates above 98%. I have contributed to projects in computer vision, NLP, and speech recognition, including labeling over 50,000 images for object detection and annotating chatbot training data. My strong attention to detail, understanding of data validation and quality assurance, and ability to meet tight deadlines make me a reliable contributor to any AI training data team.

Labeling Experience

Physics-Based AI Response Evaluation & Annotation

LabelboxTextQuestion AnsweringRLHF

Contributed to AI model improvement initiatives by evaluating and annotating advanced physics-related prompts and responses for scientific accuracy, mathematical correctness, logical consistency, and physical validity. Responsibilities included: Designing graduate-level physics problems across classical mechanics, electromagnetism, quantum mechanics, and statistical mechanics. Reviewing AI-generated solutions for multi-step mathematical derivations and theoretical soundness. Rating responses based on correctness, reasoning quality, clarity, and adherence to physical laws. Identifying logical gaps, flawed assumptions, and computational errors. Providing structured feedback to improve model reasoning and domain reliability. Project scope involved reviewing 500+ complex physics responses with strict quality benchmarks (≥95% evaluation consistency). Adhered to detailed annotation rubrics and maintained high inter-rater reliability standards.

Contributed to AI model improvement initiatives by evaluating and annotating advanced physics-related prompts and responses for scientific accuracy, mathematical correctness, logical consistency, and physical validity. Responsibilities included: Designing graduate-level physics problems across classical mechanics, electromagnetism, quantum mechanics, and statistical mechanics. Reviewing AI-generated solutions for multi-step mathematical derivations and theoretical soundness. Rating responses based on correctness, reasoning quality, clarity, and adherence to physical laws. Identifying logical gaps, flawed assumptions, and computational errors. Providing structured feedback to improve model reasoning and domain reliability. Project scope involved reviewing 500+ complex physics responses with strict quality benchmarks (≥95% evaluation consistency). Adhered to detailed annotation rubrics and maintained high inter-rater reliability standards.

2023

Education

Work History