Daniel Choi
Senior Software Engineer at Riot Games
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Choi is a Senior Software Engineer with a decade of experience building production-grade systems across cloud ML infrastructure, consumer content platforms, and VR integrity. He spent several years at Amazon contributing to SageMaker—authoring model examples, Docker integration, and test automation for TensorFlow workloads—and later worked on Kindle Manga/Webtoons ingestion pipelines. At Meta he focused on VR integrity for Quest and Horizon Worlds, and he now develops game infrastructure at Riot Games. Daniel combines deep ML deployment expertise (notably contributions to popular AWS SageMaker example repos and the SageMaker Python SDK) with hands-on testing and GPU-enabled CI work. He's based in Los Angeles and brings a pragmatic, systems-first approach grounded in formal CS training from Cal Poly Pomona. A detail that often goes unnoticed: he blends ML model engineering with tooling and QA, enabling smoother handoffs from research prototypes to scalable production containers.
10 years of coding experience
8 years of employment as a software developer
Troy High SchoolCal Poly Pomona
English, Korean
Github Skills (17)
docker10
python10
machine-learning10
dockers10
sagemaker10
amazon-sagemaker10
tensorflow10
aws10
test-automation10
testing9
jupyter-notebook8
deep-learning8
resnet8
data-science7
pytorch7
Programming languages (5)
Github contributions (5)
aws/sagemaker-tensorflow-training-toolkit
Feb 2018 - Dec 2020
Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
Role in this project:
ML Engineer & QA Engineer / Test Automation Engineer
ML Engineer & QA Engineer / Test Automation EngineerContributions:4 reviews, 18 commits, 24 PRs in 2 years 9 months
Contributions summary:Daniel contributed to the project by adding and refining functional tests, focusing on end-to-end testing on SageMaker using TensorFlow. They moved unit tests into a separate folder, indicating an organization of testing structure. Their work involved the development of testing infrastructure and resources, including the implementation of local integration tests, and modifications to existing tests to support GPU functionality via nvidia-docker, demonstrating a focus on model deployment and testing pipelines.
sagemakercontainerstensorflow-trainingdeep-learningdocker
aws/sagemaker-python-sdk
Dec 2017 - Mar 2021
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:6 releases, 78 reviews, 56 commits in 3 years 3 months
Contributions summary:Daniel made several commits focused on enhancing the Amazon SageMaker Python SDK, specifically in relation to its machine learning capabilities. They contributed to fixing hyperparameters, adding tests for tuning jobs, and increasing timeouts. The user also focused on adding support for Elastic Inference and updating versions within the SDK. These changes indicate a focus on improving the functionality and testing of the SageMaker library.
pytorchsagemakerdeployingmxnetpython
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.