André Kang-moeller
Sr. Staff Machine Learning Engineer at Reddit, Inc.
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolAndré Kang-moeller is a Sr. Staff Machine Learning Engineer in Seattle with nine years building ML platforms, inference optimization, and production tooling across AWS, Apple, NVIDIA-acquired OctoAI, and Reddit. He combines deep systems engineering with developer experience — at AWS he improved SageMaker serialization and tooling, and in open source he strengthened MXNet docs and multi-language API docs to make deep learning more accessible. André’s work spans the full ML lifecycle from containerized TensorFlow training and SageMaker examples to low-latency inference optimizations, demonstrating both MLOps and model-serving expertise. He’s comfortable shipping across languages and environments, from Dockerfiles and GPU builds to predictor serialization and integration tests. Notably, he pairs this technical breadth with a focus on usability, often improving documentation and tutorials that accelerate adoption for other engineers. Trained in CS and Philosophy at UC Berkeley, he brings analytical rigor and a multidisciplinary approach to complex ML infrastructure problems.
9 years of coding experience
9 years of employment as a software developer
Bachelor of Arts (B.A.), Computer Science, Philosophy, Bachelor of Arts (B.A.), Computer Science, Philosophy at University of California, Berkeley
Github Skills (35)
xgboost10
docker10
learn-ruby-on-rails10
python10
data-science10
user-manual10
mxnet10
machine-learning10
pyspark10
data-serialization10
basics10
dockers10
sagemaker10
serialization10
api-documentation10
Programming languages (9)
Github contributions (5)
aws/amazon-sagemaker-examples
Jan 2018 - Jul 2019
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 16 commits, 12 PRs in 1 year 6 months
Contributions summary:André primarily contributes to the development of machine learning examples within the repository, specifically focusing on integrating SageMaker with Spark and Chainer for training. The user adds examples for K-Means clustering with Spark on MNIST and XGBoost models. They also add new notebooks to the repository demonstrating machine learning model training, inference, and deployment using SageMaker.
pythonjupyter-notebooktrainingawssagemaker
aws/sagemaker-python-sdk
Feb 2018 - Oct 2020
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:4 releases, 16 commits, 36 PRs in 2 years 8 months
Contributions summary:André primarily contributed to the Amazon SageMaker Python SDK, focusing on enhancing model serialization and deployment capabilities. Their work involved modifying existing code to support Python dictionaries in the JSON serializer, which was validated through unit and integration tests. They also made modifications to the MXNet and TensorFlow predictors to support the serialization of Python lists, dictionaries, and NumPy arrays.
pytorchsagemakerdeployingmxnetpython
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