Yannis Mentekidis
Senior Software Engineer at Datadog
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolYannis Mentekidis is a Senior Software Engineer with 11 years of experience building scalable systems at Amazon, AWS, and now Datadog from his base in New York. He blends production-grade backend engineering with applied machine learning know-how, having contributed LSH search functionality and rigorous tests to the high-performance mlpack C++ library. At AWS he focused on shipping reliable cloud services, and his trajectory shows consistent progression into senior roles and system-level ownership. Yannis holds an MEng in Electrical and Computer Engineering and is pursuing executive technology management studies at Columbia, signaling a mix of deep technical skill and growing strategic perspective. Colleagues describe him as pragmatic and data-driven, with a habit of improving robustness through targeted refactoring and test-driven contributions. He brings both algorithmic rigor and production experience to projects that sit at the intersection of ML and distributed systems.
11 years of coding experience
8 years of employment as a software developer
Master of Engineering, Electrical and Computer Engineering, Master of Engineering, Electrical and Computer Engineering at Aristoteleion Panepistimion ThessalonikisMaster of Science - MS, Executive, Technology Management, Master of Science - MS, Executive, Technology Management at Columbia University in the City of New York
Github Skills (10)
algorithm10
data-structures10
algorithms10
machine-learning10
c-language10
cprogramming-language10
data-structure10
nearest-neighbors9
testing9
nearest-neighbor-search9
Programming languages (3)
Github contributions (5)
mlpack/mlpack
Mar 2016 - Jul 2017
mlpack: a fast, header-only C++ machine learning library
Role in this project:
ML Engineer
ML EngineerContributions:121 commits, 18 PRs, 3 pushes in 1 year 4 months
Contributions summary:Yannis's commits focused on adding and testing functionality related to the Locality Sensitive Hashing (LSH) search method within the mlpack library. The contributions involved creating multiple test cases that assess the performance and correctness of the LSH implementation, including tests for varying parameters like the number of tables and the hash width. These tests aimed to verify the expected behavior of the LSH algorithm in terms of recall, and the code changes reflect refactoring and optimization of the LSH search code.
regressionheaderdeep-learningscientific-computingc-plus-plus
mentekid/mlpack
Mar 2016 - Nov 2016
Contributions:1 PR, 114 pushes, 14 branches in 8 months
scalabledeep-learningmachine-learningmachine-learning-librarylibrary-learning
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