Jackson Gorham
Applied Scientist, Technical Lead at Meta
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJackson Gorham is an applied scientist and technical lead with 15 years of experience translating statistical theory into production ML systems, currently leading hearing augmentation efforts at Meta. He holds a Ph.D. in statistics from Stanford (Savage award winner) and has built low-latency, on-device audio models at Whisper.ai and Meta that run on embedded accelerators and wearable hardware. His background spans large-scale experimentation and infrastructure at Facebook, valuation modeling at Opendoor, and edge-aware signal-processing innovations, giving him a rare blend of rigorous theory (Stein’s method, convex optimization) and pragmatic engineering. Known for shipping perceptually motivated metrics and ambisonics evaluation pipelines, he focuses on robustness and deployability rather than allegiance to any single paradigm or language.
15 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at Stanford University
Github Skills (46)
python9
sampling9
machine-learning9
julia8
deep-learning8
web-audio-api8
probabilistic-models8
tensorflow8
neural-network8
unix7
r7
ml7
bash7
neovim7
deep-neural-networks7
Programming languages (5)
Github contributions (5)
jgorham/stein_discrepancy
Sep 2015 - Jul 2017
Contributions:44 commits, 33 pushes, 6 branches in 1 year 10 months
jgorham/SteinDiscrepancy.jl
May 2017 - Sep 2017
Practical tools for quantifying how well a sample approximates a target distribution
Contributions:29 commits, 3 PRs, 24 pushes in 3 months
pythonpracticalprobabilistic-modelsstochastic-processessampling
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