CV

Summary

AI systems engineer with 6+ years of experience building multimodal vision models, production ML training/serving infrastructure, and open-source model integrations in Hugging Face Transformers. Experienced in inference optimization with TensorRT/Triton, large-scale model deployment, model conversion, efficient training, and performance-oriented ML systems.

Resume downloads

• Machine Learning Engineer Resume - Clean Version (PDF)
• Software Engineer Resume - Long Version (PDF)
• Clean LaTeX source / Long LaTeX source

Open source

• Hugging Face Transformers contributor with 40+ pull requests across multimodal, vision, and object-detection models, including SAM2, Molmo2, RT-DETR, ViTPose, DINOv3 utilities, zero-shot examples, and training/inference fixes.
• Hugging Face Transformers contributor: led the addition of Segment Anything 2 (SAM2) support to huggingface/transformers.
  • Implemented and refined image/video segmentation model support, processors, documentation, conversion flow, and integration tests through a long-running community review cycle.
  • PR: Add Segment Anything 2 (SAM2); merged 2025-08-13.
  • Documentation: SAM2 model docs, credited as a model contributor.
• Hugging Face Transformers contributor: opened Add Molmo2 and published danelcsb/Molmo2-4B on the Hugging Face Hub.

Education

• M.S. in Electrical Engineering and Entrepreneurship, Korea Advanced Institute of Science and Technology, KAIST, 2020.03-2021.02
• B.S. in Electrical Engineering, Pohang University of Science and Technology, POSTECH, 2015.03-2020.02
• B.S. in Electrical and Computer Engineering, University of Illinois, Urbana and Champaign, UIUC, 2018.01-2018.12

Work experience

• 2026/01-Present: Data Scientist
  • Toss Bank (토스뱅크)
  • Building an on-premise opencode agent system for secure internal AI-assisted development workflows.
  • Developing AI-based authentication systems for face and ID card verification.
  • Developing end-to-end document information extraction systems using vision-language models for banking workflows and identity AI pipelines.
• 2021/09-2026/01: Machine Learning Engineer
  • SuperbAI
  • Led development of a multi-modal vision foundation model for text/semantic-prompted object detection and segmentation.
  • Built ML training and serving infrastructure with AWS Batch, TensorRT, and Triton Inference Server, improving inference throughput by 5x over pure PyTorch serving.
  • Built interactive segmentation tools using RepViT-SAM, FocalClick, SAM, and SAM2, increasing segmentation labeling speed by 25x.
  • Reduced GPU memory usage by 65.6% and training time by 44.3% using parameter- and memory-efficient training methods.
  • Won 2nd place in IOD and 4th place in FSOD challenges at CVPR 2025.
• 2021/02-2021/08: Machine Learning Engineer Intern
  • Kakao Enterprise
  • Fixed AutoGluon NeuralNetFastAI scaling issue.
  • Developed a Flask-based training and inference AutoML framework with a simple front-end.
• 2019/02-2020/06: Co. Team Island CTO
  • Team Island
  • Built lightweight CNN models for mobile applications and on-device inference.
• 2018/08-12: Undergraduate Researcher
  • UIUC Undergraduate Research Program
  • Improved direction-of-arrival estimation with the MUSIC algorithm using irregular microphone arrays.
  • Generated binaural sounds through a software-based audio pipeline.
  • Supervisor: Professor Romit Roy Choudhury
• 2018/06-08: Machine Learning Engineer
  • Seerslab Intern
  • Developed face landmark detection using Haar cascades, HOG features, and machine learning methods.
  • Built a GUI tool for annotating face coordinates.
  • Developed CMS login functionality with JWT-based authentication.
• 2017/03-06: Undergraduate Researcher
  • POSTECH Undergraduate Research Program
  • Developed a non-invasive heart-rate measurement device.
  • Collaborated on a smart-watch FPGA module for a national research project.
  • Supervisor: Professor Park Sung Min
• 2016/06-08: Research Intern
  • ASAN Medical Center
  • Designed simulations for an electrical surgical unit using electromagnetic field analysis tools.
  • Supported development and experiments for an assistive device for knee-injured patients.
  • Supervisor: Professor Choi Jae Soon

Skills

• AI systems: PyTorch, Hugging Face Transformers, TensorRT, Triton Inference Server, AWS Batch, MLflow, model conversion
• Machine learning: computer vision, multimodal vision models, object detection, image/video segmentation, pose estimation, efficient training
• Programming: Python, Kotlin, Git
• Deployment: mobile ML, lightweight CNNs, on-device inference, TensorFlow Lite
• AI developer tools: Claude Code, Codex, opencode, LangChain, Langfuse
• Hardware and tools: FPGA development with Xilinx, PyQt5

Publications

Terahertz Signal Analysis in Tissue of Gastroesophageal Reflux Disease

Choi Sang Bum. (2013). &Terahertz Signal Analysis in Tissue of Gastroesophageal Reflux Disease." OPTICAL SOCIETY OF KOREA. POSTER SESSION I(WP-VI3).

On Joint Transfer of Energy and Information: A Markov Decision Problem Formulation

Sangbum Choi. (2018). "On Joint Transfer of Energy and Information: A Markov Decision Problem Formulation."None.

Improved DOA estimation of MUSIC algorithm based on irregular microphone array

Sangbum Choi. (2018). "Improved DOA estimation of MUSIC algorithm based on irregular microphone array." None. .

MobileHumanPose: Toward Real-Time 3D Human Pose Estimation in Mobile Devices

Sangbum Choi, Seokeon Choi, and Changick Kim. (2021). "MobileHumanPose: Toward Real-Time 3D Human Pose Estimation in Mobile Devices." IEEE/CVF CVPR Workshops, 2328-2338.

ZERO: Multi-modal Prompt-based Visual Grounding

Sangbum Choi and Kyeongryeol Go. (2025). "ZERO: Multi-modal Prompt-based Visual Grounding." arXiv.

Talks

Teaching

Electric Circuit

Linear Algebra

Athlete Service and Leadership

• CTO, Team Island, 2019/02-2020/06
• Open-source contributor to Hugging Face Transformers