Eduardo Salinas
Senior Software Engineer at Microsoft
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolEduardo Salinas is a Senior Software Engineer in New York with 22 years of experience building high-scale backend systems, ML tooling, and training infrastructure for frontier AI at Microsoft Research. He bridges research and production—contributing to open-source projects like Vowpal Wabbit and Microsoft Bond while implementing robust back-end, DevOps, and tracing improvements in agentic AI tooling. At Microsoft he has designed infrastructure for efficient evaluation and training of multimodal LLMs on Kubernetes clusters using both NVIDIA and AMD GPUs, and helped operationalize reinforcement learning systems for real-world personalization. Known for pragmatic refactors and reliability work, he often surfaces subtle data-validation and cross-platform encoding fixes that prevent costly failures in production.
22 years of coding experience
9 years of employment as a software developer
Computer Science, Computer Science at Tecnológico de Monterrey
Github Skills (30)
cpp-library10
c-language10
python10
agentic10
elearning10
autogen10
machine-learning10
data-serialization10
xml-schema10
standard-library10
llm10
serialization10
code-generation10
c-libraries10
bandit10
Programming languages (17)
Github contributions (5)
VowpalWabbit/vowpal_wabbit
May 2019 - Jan 2023
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Role in this project:
Backend Developer & ML Engineer
Backend Developer & ML EngineerContributions:1 release, 1015 reviews, 298 commits in 3 years 8 months
Contributions summary:Eduardo primarily contributed to the core functionalities of Vowpal Wabbit, focusing on refining and improving existing algorithms and infrastructure. Their commits involve refactoring and refactoring code related to the contextual bandit (CB) and other core parts of the library. Furthermore, they updated the build process and adding various flags to improve functionality. The commits indicate a deep understanding of the codebase and its underlying machine learning techniques, specifically in areas like Contextual Bandits and Online Learning.
hashingtechniquescpppythonactive-learning
microsoft/bond
Jan 2016 - May 2022
Bond was a cross-platform framework for working with schematized data. The open-source project ended on March 31, 2025.
Role in this project:Back-end Developer
Back-end DeveloperContributions:3 releases, 25 reviews, 79 commits in 6 years 5 months
Contributions summary:Eduardo primarily contributed to the Bond library, focusing on improving its core functionality and robustness. They implemented checks for data validation, specifically related to field ordinals and duplicate field names within the library. Furthermore, the user addressed bugs, enhanced generic support within bond_meta fields, and added support for parsing service definitions. These contributions indicate a focus on the core data modeling and schema definition aspects of the Bond framework.
dotnetcross-languageserializationscalegeneric
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