David Strauss
Machine Learning Engineer at Meta
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolDavid Strauss is a Machine Learning Engineer with 14 years of experience applying optimization, matrix methods, and Bayesian modeling to production-scale problems. He holds a PhD in Electrical Engineering from Stanford and has moved from academic inverse-problem research into industry roles shaping data products and ML at Euclid, Clara Lending, Oliver Wyman (where he rose to Principal Data Scientist), and now Meta. David builds end-to-end solutions—data pipelines, scalable models, and large-scale optimization—for consumer and enterprise analytics, with a particular knack for marrying rigorous mathematical approaches to messy real-world data. Based in Boston, he blends research-level signal processing and optimization expertise with consulting experience in translating technical insight into business impact. An under-the-radar strength is his comfort with low-level numerics and matrix-heavy algorithms, which he uses to squeeze extra performance out of production models.
14 years of coding experience
14 years of employment as a software developer
Bachelor of Arts (B.A.), Physics, Bachelor of Arts (B.A.), Physics at Dartmouth CollegeSt. Mark's School of TexasSt. Mark'sPhD, Electrical Engineering, PhD, Electrical Engineering at Stanford University
Spanish, Hebrew
Github Skills (5)
surface8
optimization6
imaging6
scala2
spark2
Programming languages (1)
Github contributions (5)
daStrauss/sparseSeparation
Aug 2012 - Apr 2013
Contributions:18 commits in 7 months
daStrauss/sparseConv
Dec 2012 - Jul 2013
Contributions:66 commits in 6 months
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