Beomjin Seo

Beomjin Seo

AI Research Engineer

서범진 · Seoul, Republic of Korea

I build and study large-scale models, with a focus on training and evaluating on-device LLMs and LMMs. My work spans efficient multi-node pretraining, vision–text multimodal learning, and post-training from supervised fine-tuning to reinforcement-learning variants. I care about making capable models small and efficient enough to run where people actually use them.

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Projects

On-device · LLM

LLM Pretraining for Efficient On-device Model

Mar 2026 – Present
  • Experimented with efficient training setups in multi-node GPU environments.
  • Ran distributed-training experiments using Megatron-LM.
On-device · LMM

LMM Pretraining for On-device Model

Jan 2026 – Present
  • Jointly trained and experimented with vision and text modalities.
  • Proposed a training methodology that accounts for quantization.
Post-training · RL

LLM Post-training: from SFT to RL Variants

Mar 2025 – Dec 2025
  • Ran experiments spanning SFT for large-scale models to RLVR and OPD-style methods.
GNN · Drug Discovery

Binding Affinity Prediction using Graph Neural Networks with Attention

May 2020 – Aug 2020
  • As main researcher, developed a graph attentional model to predict binding affinity between proteins and ligands; it outperformed other ML-based baselines.
  • Provided a visual explanation via attention maps, confirming the model focused on biologically meaningful binding positions.

Work Experience

Samsung Research — Full time

AI Core Team · Engineer

Jul 2022 – Present
  • Training & Evaluating LLMs: trained LLMs and LMMs on multi-node GPUs, evaluated across multiple benchmarks, and ran post-training (data-mix strategies, synthetic-data generation pipelines, agentic model development via RLVR and OPD-style methods).
  • Semantic Deep Search for TV manuals: prepared domain-specific datasets, trained task-specific deep-search models, and compressed model weights.

Kim Jaechul Graduate School of AI at KAIST — Intern

Research Intern, Edward's Lab (mentor: Prof. Edward Choi)

Jul 2021 – Aug 2021
  • Developed a multimodal dataset from Wikipedia and ran a proof-of-concept with it.

KIST Europe — Intern

Research Intern, Smart Convergence Group (mentor: Dr. Sangrak Lim) · Saarbrücken, Germany

Feb 2020 – Aug 2020
  • Worked on QSAR model development and molecular docking using graph attentional network models.

Education

Kyung Hee University

B.S. in Software Convergence & Biomedical Engineering · GPA 4.1 / 4.5

Mar 2015 – Feb 2022

CV

Curriculum Vitae

Full résumé — updated June 2026 (PDF).

Open CV (PDF) →