Palmer Lao is a software engineer in San Francisco with 12 years of experience building production data and ML systems, currently on the Google engineering team. He blends a strong academic background in mathematics and statistics with hands-on work at Formation, where he led large migrations, built multi-tenant offer execution components, and optimized hyperparameter pipelines and model serving for production. Palmer’s strengths include scalable data processing, Bayesian analysis, and functional programming, and he has a track record of improving operational reliability, performance, and observability across services. He has practical experience shifting tech stacks, extending deployment tooling (including AWS Lambda workflows), and reducing costs and runtime for critical data jobs. Colleagues know him for translating research techniques—MCMC, probabilistic models, and numerical methods—into robust, automated pipelines. He’s quietly effective at turning academic ideas into operational systems that scale.
12 years of coding experience
6 years of employment as a software developer
Master's Degree, Statistics, 3.7/4.0, Master's Degree, Statistics, 3.7/4.0 at Columbia University in the City of New York
Bachelor's Degree, Mathematics and Computer Science, 3.85/4.0, Bachelor's Degree, Mathematics and Computer Science, 3.85/4.0 at Clarkson University
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
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