Seth Hendrickson
Staff Software Engineer at Stripe
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolSeth Hendrickson is a Staff Software Engineer at Stripe with 11 years of experience bridging electrical engineering, data science, and distributed machine learning. He moved from an applied EE background into scalable ML systems, contributing as an Apache Spark committer where he improved Spark ML—adding decision tree feature importances and optimizing logistic regression performance. At Stripe and prior roles at Cloudera and IBM he builds production-grade, distributed ML infrastructure and back-end systems in Scala and Spark. Based in San Francisco, he combines research-oriented rigor with pragmatic engineering to ship performant, scalable models and APIs. An active open-source contributor and Spark committer, he brings deep knowledge of large-scale analytics engines that many teams rely on in production.
11 years of coding experience
10 years of employment as a software developer
Master of Science (M.S.), Electrical and Electronics Engineering, Master of Science (M.S.), Electrical and Electronics Engineering at Georgia Institute of TechnologyBachelor of Science (B.S.), Electrical and Electronics Engineering, Bachelor of Science (B.S.), Electrical and Electronics Engineering at Rose-Hulman Institute of Technology
English, Spanish
Github Skills (12)
mle10
javas10
decision-tree10
logistic-regression10
machine-learning10
spark10
java10
ml10
scala10
algorithms9
data-structures8
data-structure8
Programming languages (8)
Github contributions (5)
apache/spark
Jun 2015 - Feb 2020
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:61 PRs, 1528 comments in 4 years 8 months
Contributions summary:Seth primarily contributed to the Apache Spark ML library, demonstrating strong expertise in machine learning model development. The user implemented feature importances for decision trees, adding API entry points and refining the calculation of feature importance. They also refactored and improved code related to logistic regression and other ML algorithms, introducing performance optimizations and addressing code serialization. These changes demonstrate a focus on improving and extending Spark's machine learning capabilities.
analyticspythondata-processingsqlapache
sethah/Data-Science-Resources
Jan 2015 - Aug 2015
Contributions:5 commits, 4 pushes in 6 months
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