Sunghwan Kim
머신 러닝 엔지니어 at ScatterLab
Incheon, South Korea
Summary
👤
Senior🎓
Top SchoolSunghwan Kim is a machine learning engineer with 8 years of experience building and operating production-grade ML systems and LLM serving infrastructure. At ScatterLab he optimized LLM serving for a conversational AI product—cutting serving costs by 45%—and built a multi-cloud Kubernetes operator to autoscale GPUs across seven+ environments. He designs end-to-end MLOps and data pipelines (Airflow, Apache Beam, BigQuery) for training, pseudonymization at nTB scale, and continuous model learning, and has shipped a customer-facing Pingpong AI platform with training-as-a-service capabilities. Previously he developed real-time on-device audio source separation engines (TensorFlow Lite/CoreML/C++) and improved inference speed via quantization and NNAPI. Comfortable across backend (Kotlin/Spring Boot), infra, and model optimization, he blends low-level performance tuning with large-scale orchestration. Outside work he pursues music and audio processing, informing his pragmatic approach to audio-ML problems.
8 years of coding experience
학사, 컴퓨터소프트웨어학부, 학사, 컴퓨터소프트웨어학부 at 한양대학교
Github Skills (37)
rl-environment10
hearthstone10
vscode10
reinforcement-learning9
ide9
development-environment9
python-api9
cpp9
vscode-remote7
tensorflow-lite6
react5
multistage5
java5
ios4
react-native4
Programming languages (8)
Github contributions (5)
FYLSunghwan/react-native-pitch-tracker
Sep 2020 - Nov 2020
React Native Pitch Tracker implemented with Tensorflow Lite
Contributions:5 releases, 67 commits, 18 PRs in 2 months
react-nativetensorflow-lite
FYLSunghwan/react-native-piano-sampler
Sep 2020 - Oct 2020
React Native Piano Sampler : Reads sf2 with FluidSynth(on Android) or sfz with AudioKit(on iOS)
Contributions:4 releases, 46 commits, 12 PRs in 1 month
androidiosreact-native
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