Aaron Markham
Sr. Machine Learning Engineer Sr. Data Scientist
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolAaron Markham is a Machine Learning Engineer in Seattle with nine years of hands-on experience building robust ML systems that perform reliably under imperfect conditions. He has deep expertise in model deployment and computer vision—contributing to AWS's Multi Model Server and SageMaker examples—and has worked extensively with MXNet, SSD models, and inference-serving pipelines. At AWS he has led efforts around knowledge graphs, dataset generation, and personalization for LLM training and search, blending engineering, product, and research priorities. His background spans technical writing, documentation leadership, and open-source contributions, making him effective at translating complex ML systems into reproducible examples and clear docs. Earlier roles as a founder and R&D leader yielded multiple patents in image processing, reflecting an uncommon mix of entrepreneurial product instincts and production ML engineering. He pairs practical Python and cloud-native tooling skills (CDK, LangChain) with a knack for improving developer experience and operational reliability.
9 years of coding experience
25 years of employment as a software developer
Bachelor’s Degree, Enology, Chemistry, Bachelor’s Degree, Enology, Chemistry at California State University, Fresno
English, Spanish
Stackoverflow
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Github Skills (32)
continuous-deployment10
python10
jupyter10
data-science10
sphinx10
mxnet10
machine-learning10
inference10
ml-deployment10
amazon-sagemaker10
deep-learning10
aws10
computer-vision10
jupyter-notebook10
documentation10
Programming languages (11)
Github contributions (5)
awslabs/multi-model-server
Oct 2017 - Jun 2019
Multi Model Server is a tool for serving neural net models for inference
Role in this project:
ML Engineer
ML EngineerContributions:90 commits, 28 PRs, 30 pushes in 1 year 8 months
Contributions summary:Aaron primarily contributes to the `dms` directory, modifying files related to model export, and model service. They add and modify preprocessing and postprocessing methods, specifically for SSD models, indicating expertise in computer vision and deep learning model deployment. They also address issues related to MXNet model loading and usage, suggesting familiarity with the framework. These changes are consistent with the creation and improvement of an inference server for deep learning models.
inferencemxnetdeep-learningaineural-network
aws/amazon-sagemaker-examples
Feb 2020 - Apr 2022
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:1 release, 334 reviews, 30 commits in 2 years 2 months
Contributions summary:Aaron primarily contributed to example notebooks within the Amazon SageMaker examples repository, focusing on machine learning model training and deployment using Amazon SageMaker Studio. Their work involved updating notebooks to include new features, fixing input shapes for prediction, and integrating examples for using default buckets. Furthermore, the user addressed image and text processing in the repository, which included implementing and showcasing model training and deployment best practices.
amazon-sagemakerjupyter-notebookmachine-learning-modelssagemakeraws
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