Machine Learning Engineer in Seoul.
I am interested in vectorizing reality, making predictions, and estimating uncertainty.
Over seven years, I have worked with public-procurement, construction, and retail data, connecting tabular ML, NLP, computer vision, and LLM agents from data collection through evaluation and deployment. I quickly learn unfamiliar domains and inherited systems, then turn data, models, and operating workflows into working services. I build systems that balance predictive performance with reliability, cost, and operational constraints.
- Rapid problem understanding and execution: I learn unfamiliar domains and inherited systems quickly, identify the highest-impact problems, and turn them into operable ML systems.
- Model diagnosis and redesign: I trace errors and performance bottlenecks through experiments, then adjust training, inference, and evaluation for the real operating context.
- Uncertainty-aware decisions: I use distributions, intervals, calibration, and simulation to communicate reliability and risk rather than returning only a point prediction.
- Research-to-production translation: I adapt new methods to real data, infrastructure, cost, and operating constraints and implement them as repeatable pipelines.
- End-to-end delivery: I connect data construction, training, evaluation, batch jobs, APIs, and service operations in a form that others can review, maintain, and extend.