Milo Webster
Machine Learning Engineer at Gridmatic
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolMilo Webster is a machine learning engineer based in Berkeley with 11 years of hands-on experience blending signal processing, embedded systems, and software development. He holds a Master of Engineering in Signal Processing from UC Berkeley and has applied that expertise to production systems—from optimizing C++ channel synchronization at Boeing to integrating radio decoders and ML workflows at Gridmatic. Milo’s background includes founding work on a Formula SAE electric team where he wrote real-time firmware for STM32 MCUs and designed a peripheral BMS that broadcasts cell temps over CANOpen. He’s comfortable across the stack—firmware, backend APIs, and deployment—and has shipped low-latency ETL and asset-tracking systems in startup environments. Known for squeezing performance and memory out of signal-processing code, he combines academic rigor with practical product delivery. His GitHub hosts several project repositories that reflect a continual focus on embedded monitoring, Raspberry Pi automation, and reusable Python frameworks.
11 years of coding experience
7 years of employment as a software developer
Master of Engineering - MEng, Electrical Engineering and Computer Science - Signal Processing, Master of Engineering - MEng, Electrical Engineering and Computer Science - Signal Processing at University of California, BerkeleyWest Linn High SchoolUniversity of California Santa Cruz
English, French
Github Skills (52)
ember-addon9
ember-simple-auth9
python8
jwt8
pipeline8
machine-learning8
kubeflow8
data-science7
jupyter7
token-based-authentication7
kubernetes7
notebook7
gpu7
ember7
keras7
Programming languages (5)
Github contributions (5)
formulaslug/fs-1-cell-temp
Feb 2017 - Jun 2018
Contributions:3 PRs, 21 pushes, 13 branches in 1 year 3 months
batterycan-bustempbusmicrocontroller
mmwebster/voxsrc-2020
Jun 2020 - Nov 2020
Codebase for 2020 VGG Speaker Recognition Challenge. Contains source for ML pipeline and training/experiment infrastructure
Contributions:153 commits, 1 PR, 2 pushes in 5 months
machine-learningspeaker-recognition
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