Senior Software Engineer, Machine Learning at Meta
Seattle, Washington, United States
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Summary
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Senior
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Top School
Tianshi Wang is a senior machine learning engineer at Meta with eight years of experience building high-performance quantitative models and production systems across finance, VR recommendations, and materials science. He earned a Ph.D. in computational materials science and has translated that rigorous scientific background into production-grade implementations of complex models—ranging from SABR forward-rate and LIBOR Market Models in C++/CUDA to scalable ML systems in Python. At BlackRock and Chatham he led the design and deployment of low-latency valuation engines and Monte Carlo simulators integrated into enterprise platforms, and at Meta he now applies those skills to VR home feed recommendations. Comfortable across object-oriented design, distributed systems, and HPC, he also brings hands-on QA and automation experience from open-source education projects. Colleagues rely on him for bridging deep quantitative research with pragmatic engineering to deliver reliable, high-performance production software.
8 years of coding experience
7 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Georgia Institute of Technology
Doctor of Philosophy (Ph.D.), Materials Science, 3.91/4, Doctor of Philosophy (Ph.D.), Materials Science, 3.91/4 at University of Delaware
Master of Science (M.S.), Materials Science, Master of Science (M.S.), Materials Science at Huazhong University of Science and Technology
Expertiza is a web application through which students can submit and peer-review learning objects (articles, code, web sites, etc). The Expertiza project is supported by the National Science Foundation.
Role in this project:
Back-end Developer & QA Engineer / Test Automation Engineer
Contributions:5 commits in 1 day
Contributions summary:Tianshi focused on testing and improving the sign-up sheet controller within the Expertiza application. Their contributions involved writing and modifying test cases using RSpec to cover various scenarios related to user sign-up, instructor actions, and topic selection. The user's work ensures the correct behavior of critical functions, including actions triggered during the sign-up process and actions taken by instructors.
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Tianshi Wang - Senior Software Engineer, Machine Learning at Meta