Tom Faulhaber
Director, Software Engineering at Axon
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTom Faulhaber is a seasoned software engineering leader with 17+ years delivering cloud-native, ML, and real-time systems across startups and Amazon, now directing engineering at Axon from San Francisco. He blends deep technical craftsmanship—evidenced by contributions to Amazon SageMaker examples and BYO ML containers—with product-driven leadership that emphasizes structured learning and metric-led decision making. Tom has repeatedly shifted between hands-on engineering, principal roles, and executive leadership to maximize impact, from building creator-focused AMP features to architecting large-scale video and geospatial platforms. He mentors teams to discover and execute on high-potential ideas, translating research and analytics into production-ready services. Unusually, his career spans early internet protocol work through modern ML deployment patterns, giving him a rare end-to-end perspective on scalable systems.
17 years of coding experience
30 years of employment as a software developer
B.A. Applied Sciences and Engineering, B.A. Applied Sciences and Engineering at Harvard University
clojure
Stackoverflow
73reputation
10kreached
1answer
1question
Github Skills (20)
docker10
python10
scikit10
machine-learning10
inference10
dockers10
sagemaker10
amazon-sagemaker10
scikit-learn10
jupyter-notebook10
aws9
trainings9
mlops8
data-science8
emacs6
Programming languages (9)
Github contributions (5)
aws/amazon-sagemaker-examples
Nov 2017 - Dec 2018
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:16 commits, 2 comments, 1 issue in 1 year
Contributions summary:Tom's primary contribution involves developing and refining a bring-your-own (BYO) example within the Amazon SageMaker environment. This includes implementing a decision tree model from the scikit-learn library and integrating it into a Docker container. Further, the user is adding content and structure to the Jupyter notebook that describes how to use the BYO container for both training and hosting on SageMaker. This demonstrates expertise in packaging machine learning models for cloud deployment.
pythonjupyter-notebooktrainingawssagemaker
incanter/incanter
Dec 2009 - May 2016
Clojure-based, R-like statistical computing and graphics environment for the JVM
Contributions:144 commits, 8 pushes in 6 years 6 months
statisticaljvmcomputingclojuregraphics
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