Tyler Yep
Senior Software Engineer at Robinhood
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTyler Yep is a Senior Software Engineer based in Palo Alto with 10 years of experience building full‑stack systems and production ML tooling. A Stanford CS graduate, he has advanced backend and deep learning expertise—especially in Python, PyTorch, TensorFlow/Keras—and has contributed notable open‑source work improving PyTorch model summaries. At Robinhood he progressed from intern to senior engineer, shipping APIs and front-end integrations that support high‑stakes finance flows. His background spans scalable web services, frontend frameworks (React/Redux, d3.js), and low‑level languages like C++, reflecting an ability to move between research code and production systems. He also brings teaching experience from Stanford’s CS program and hands‑on VR/data work, which informs a pragmatic but research‑minded approach to problem solving.
10 years of coding experience
5 years of employment as a software developer
Bachelor's degree Master's Degree Computer Science, Bachelor's degree Master's Degree Computer Science at Stanford University
Stackoverflow
37reputation
1kreached
3answers
4questions
Github Skills (15)
pytorch10
python10
pytest9
unit-test9
unit-testing9
machine-learning7
deep-learning7
git6
html6
dimensions6
css6
data-structures6
function-call6
create-react-app6
github6
Programming languages (13)
Github contributions (5)
TylerYep/torchinfo
Mar 2020 - Nov 2022
View model summaries in PyTorch!
Role in this project:
Back-end Developer
Back-end DeveloperContributions:33 releases, 35 reviews, 242 commits in 2 years 8 months
Contributions summary:Tyler primarily contributed to the `torchinfo` project, focusing on model summarization in PyTorch. Their commits demonstrate an effort to integrate the existing `torchsummary` library into the project. Their main contribution involves refactoring and improving the existing model summary functions by incorporating and modifying code from a similar library, indicating a strong understanding of PyTorch model internals and related libraries, and the user also demonstrates an ability to work with existing integration with libraries such as pytest.
pytorchpythondeep-learningtorchtorchinfo
TylerYep/probs
Sep 2020 - Mar 2025
Intuitive Probability API in Python
Contributions:21 PRs, 68 pushes, 23 branches in 4 years 6 months
distributionsprobabilityprobspythonrandom-variables
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