Chris Song
Senior Data Engineer
Seoul, South Korea
Summary
🤩
Rockstar🎓
Top SchoolChris Song is a Senior Data Engineer and engineering leader based in Seoul with over a decade of experience building cloud-native big data and MLOps systems. He has a proven track record scaling real-time streaming pipelines (Kafka), unifying batch and streaming codebases, and turning predictive ML into revenue—most notably running daily price predictions for ~65M NFT tokens and cutting BigQuery costs from $120k to $40k monthly. Comfortable across the stack, he has driven dramatic MLOps cost optimizations (reducing pipeline ops from ~$10k to ~$100 monthly), introduced dbt for data quality, and led AutoML, HPO and NAS efforts at NAVER. Chris is an active open-source contributor and tester in ML tooling—helping improve JAX-based dm-haiku tests and publishing Deep RL examples for PySC2—demonstrating a blend of research, production engineering, and attention to reliability. His background in innovation and technology management from KAIST complements hands-on productization skills and a knack for mentoring engineers to deliver measurable business impact.
11 years of coding experience
5 years of employment as a software developer
Korea Digital Media High SchoolMaster's degree, Innovation & Technology Management, Master's degree, Innovation & Technology Management at Korea Advanced Institute of Science and Technology
Korean, Chinese, English, Japanese
Github Skills (15)
deep-reinforcement-learning10
machine-learning10
tensorflow10
pytest10
text-editing10
python10
reinforcement-learning10
documentation10
testing10
faster-rcnn9
jax9
deep-neural-networks9
deeplearning-ai9
deep-learning9
mask-rcnn9
Programming languages (9)
Github contributions (5)
chris-chris/pysc2-examples
Jan 2017 - Mar 2020
StarCraft II - pysc2 Deep Reinforcement Learning Examples
Role in this project:
ML Engineer
ML EngineerContributions:184 commits, 6 PRs, 178 pushes in 3 years 2 months
Contributions summary:Chris focused on developing examples for deep reinforcement learning within the context of the StarCraft II PySC2 environment. They implemented and modified deep Q-learning models, particularly in the `sc2_deepq.py` file, which was a core contribution. Their work included adjusting the action space, processing screen features, and defining the training process for a deep Q-network in this StarCraft II environment. Further development included modifying the convolution layers and reward settings.
starcraftstarcraft-iiactor-criticdeep-learningreinforcement-learning
google-deepmind/dm-haiku
Feb 2020 - Mar 2020
JAX-based neural network library
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:18 commits, 9 PRs, 16 comments in 1 month
Contributions summary:Chris primarily contributed to the testing and quality assurance of the `dm-haiku` library. They implemented multiple test cases, including unit tests for reshape, moving averages, and module components. Their work involved adding tests for edge cases, invalid inputs, and ensuring code coverage. Furthermore, the user made changes to the existing test infrastructure and applied feedback related to test coverage and quality.
deep-learningneural-networksmachine-learningneural-networkdeep-neural-networks
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