Santiago Castro
Research Scientist at Netflix
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSantiago Castro is a Research Scientist at Netflix with 12 years of experience bridging academic rigor and industry impact in vision-and-language and video representation learning. He holds a PhD in Computer Science from the University of Michigan and a track record of multiple Netflix, Google, and Adobe research internships focused on compositional generalization and language-guided video representations. Santiago combines deep research skills with pragmatic engineering—contributing bug fixes, refactors, and performance-focused changes to major open-source projects like Hugging Face datasets, Transformers, and Scenic. Earlier in his career he founded and led ML efforts at Xmartlabs, creating Bender, an open-source framework for real-time neural networks on iOS that presaged later platform offerings. Based in San Francisco, he brings both production-grade code hygiene and a history of improving documentation and reliability across NLP and CV toolchains. He’s equally comfortable publishing novel research and rolling up his sleeves to maintain large community libraries.
12 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of MichiganHigh School Diploma Engineering, High School Diploma Engineering at Escuela y Liceo Elbio FernándezBachelor of Engineering - BE Computer Science, Bachelor of Engineering - BE Computer Science at Universidad de la República
Spanish, English
Stackoverflow
5,940reputation
423kreached
27answers
7questions
android
top-5%
kotlin
top-5%
okhttp
top-5%
cuda
top-1%
gcc
top-5%
Github Skills (72)
transformers10
python10
flax10
datasets10
metal10
deep-learning10
apple210
ml10
computer-engineering10
neural-network10
computer-vision10
transformer10
swift10
csv10
nlp10
Programming languages (31)
Github contributions (5)
Lightning-AI/torchmetrics
Aug 2020 - Oct 2022
Machine learning metrics for distributed, scalable PyTorch applications.
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 17 commits, 10 PRs in 2 years 3 months
Contributions summary:Santiago primarily contributed to fixing various issues and improving the documentation related to machine learning metrics within the `torchmetrics` repository. Their work involved correcting typos in documentation, fixing error messages, removing unnecessary warnings, and adding a new metric (Perplexity). They also addressed code-related issues by modifying existing metric implementations.
pytorchscalablepythonanalysesdata-science
explosion/spaCy
Apr 2019 - Jun 2021
💫 Industrial-strength Natural Language Processing (NLP) in Python
Role in this project:Software Engineer (Focused on NLP & Python)
Software Engineer (Focused on NLP & Python)Contributions:8 commits, 8 PRs, 4 comments in 2 years 2 months
Contributions summary:Santiago primarily contributed to bug fixes and improvements within the spaCy library. They addressed issues in core utilities like `minibatch`, corrected typos in documentation (language docstrings and training CLI), and resolved a double-slash issue in the model release web page. Their work also involved fixing typos in comments and making minor adjustments to the attribute ruler. These contributions demonstrate a focus on code quality, documentation accuracy, and overall library stability.
fairness-mlpythondata-preprocessinglanguage-processingtokenization
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