Po-wei Wang
Applied Scientist at Pinterest
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolPo-wei Wang is an applied scientist and machine learning engineer with 11 years of experience, currently driving GPU-first model deployment and efficiency at Pinterest from Palo Alto. He led the MLEnv migration that moved all production models to GPU—delivering over 300x acceleration while preserving latency and budget—and launched Pinterest’s first user-sequence model. His expertise spans GPU benchmarking, CUDA development, and building a company-wide CUDA acceleration package that materially sped up training and serving. With a Ph.D. in machine learning from Carnegie Mellon, he combines deep research (ICML recognition, publications and patents) with practical systems work, including prior deep-learning search and neural reasoning contributions at A9 and Bosch. Colleagues rely on him to translate cutting-edge models into production-ready, cost- and latency-aware systems.
11 years of coding experience
1 year of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at National Taiwan UniversityThe Affiliated Senior High School of National Taiwan Normal UniversityDoctor of Philosophy (Ph.D.) Machine Learning, Doctor of Philosophy (Ph.D.) Machine Learning at Carnegie Mellon University
English, Chinese
Github Skills (14)
solver10
satisfiability9
marketplace8
pytorch7
deep-learning7
convex5
performance-counters4
cpu4
hardware4
julia4
profiling4
hardware-performance-counters3
cuda3
gpu3
Programming languages (3)
Github contributions (5)
xflash96/epsilon
Sep 2015 - May 2016
Contributions:85 pushes, 3 branches in 8 months
convexsolverproximal-operatorsjulia
locuslab/SATNet
May 2019 - Nov 2022
Bridging deep learning and logical reasoning using a differentiable satisfiability solver.
Contributions:16 commits, 1 PR, 9 pushes in 3 years 6 months
pytorchdifferentiablesatisfiabilitydeep-learninglogical-reasoning
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