Henry Peteet
Senior Software Engineer at Freenome
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolHenry Peteet is a Senior Software Engineer in San Francisco with 11 years of experience building reliable cloud infrastructure and tooling across companies like Freenome, Unity Technologies, and Google. He specializes in backend and DevOps work for ML and distributed systems, having improved stability in Unity's widely used ML-Agents toolkit by hardening environment management and worker recovery. At Google he built low-overhead monitoring and tooling for the Borg ecosystem and earlier focused on performance and security tooling during multiple internships. A former Head TA, he brings a knack for clear technical communication and practical teaching, which complements his passion for education and gaming-driven ML. Colleagues rely on him to turn brittle training and production pipelines into robust, observable services.
11 years of coding experience
4 years of employment as a software developer
Bachelor of Applied Science (BASc), Computer Science, 4th Year, Bachelor of Applied Science (BASc), Computer Science, 4th Year at Georgia Institute of TechnologyComputer Science and Web Apps, Computer Science and Web Apps at Google CSSI (Computer Science Summer Institute)Henry W. Grady High School
Stackoverflow
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Github Skills (13)
deep-reinforcement-learning10
python10
subprocess10
unity3d9
machine-learning8
devops8
githubaction-workflow7
reinforcement-learning7
github-ci7
neural-network7
docker6
dockers6
pytest5
Programming languages (4)
Github contributions (5)
Unity-Technologies/ml-agents
Aug 2020 - Feb 2022
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:31 reviews, 28 commits, 10 PRs in 1 year 6 months
Contributions summary:Henry's contributions center around improving the reliability and stability of the ML-Agents toolkit's environment management and training infrastructure. They focused on addressing issues with subprocess environment managers, implementing worker restart mechanisms, and fixing errors in the analytics system. These changes involved modifications to Python scripts and the addition of code guards related to Unity analytics to prevent compilation errors, ensuring that the training pipeline is robust.
servedeep-reinforcement-learningtrainingunityreinforcement
hvpeteet/golang-fractal-generator
Jul 2015 - Jun 2018
Contributions:5 commits, 4 pushes, 1 branch in 2 years 11 months
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