Eduardo Salinas

New York, New York, United States

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Summary

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Rockstar

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Top School

Eduardo Salinas is a Senior Software Engineer in New York with 21 years of experience, currently at Microsoft where he bridges machine learning, backend systems, and cloud infrastructure. He contributes to Vowpal Wabbit—bringing improvements to contextual bandits and online learning—and helped harden Microsoft’s Bond schema framework with core data validation and service parsing work. His projects span ML engineering, AKS/cloud services, and DevOps, including Windows encoding fixes and enhanced event tracing for agentic AI tooling, reflecting a focus on reliability and observability in production. Based at Microsoft Research NYC since 2019, he pairs research-grade algorithmic insight with pragmatic backend and build-system improvements. A Tecnológico de Monterrey computer science alumnus, he thrives at the intersection of low-level data modeling and large-scale ML systems.

22 years of coding experience

9 years of employment as a software developer

Computer Science, Computer Science at Tecnológico de Monterrey

Github Skills (34)

cpp-library10

c-language10

python10

agentic10

elearning10

machine-learning10

autogen10

data-serialization10

xml-schema10

standard-library10

llm10

serialization10

code-generation10

c-libraries10

bandit10

Programming languages (17)

C#PowerShellJavaC++RustGoHTMLTypeScript

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

Contributions: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

Contributions: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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