Ivan Rodriguez
Engineering Manager at Hacker Noon
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolIvan Rodriguez is an engineering manager with 10 years of experience leading teams that build highly scalable distributed systems and data platforms, currently managing Google.org’s Technology Team in Seattle. He has a track record of driving user engagement and reliability—at Microsoft he led Bing Homepage initiatives that boosted engagement and at Amazon he raised data pipeline reliability from 91% to 99.9% while scaling an ETL platform for tens of thousands of internal customers. A hands-on leader and mentor, Ivan has built and grown engineering teams, conducted hundreds of interviews, and coached engineers to multiple promotions. He combines system-level performance insight (contributing backend work to Microsoft’s CNTK toolkit) with public-facing technical writing as the founder of a long-running tech blog and contributor to Hacker Noon. His background in AI and grid computing underpins a pragmatic approach to product-driven engineering and measurable impact.
9 years of coding experience
12 years of employment as a software developer
Universitat Politècnica de ValènciaMaster of Science (MSc) Computational and Software Techniques in Engineering: Grid Computing, Master of Science (MSc) Computational and Software Techniques in Engineering: Grid Computing at Cranfield University
English, Spanish, Catalan
Github Skills (8)
cntk10
performance-optimization10
python10
machine-learning9
code-analysis9
deep-learning9
c-language8
cprogramming-language8
Programming languages (2)
Github contributions (5)
microsoft/CNTK
Jun 2016 - Sep 2017
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Role in this project:
Backend Developer
Backend DeveloperContributions:258 commits, 219 pushes, 115 branches in 1 year 4 months
Contributions summary:Ivan focused on printing hardware information from the CPU and GPU in the Microsoft Cognitive Toolkit (CNTK), which suggests a focus on performance and system-level understanding. They extracted information from baseline files, likely to compare model performance across different hardware configurations. Furthermore, they implemented the collection of model metrics into a CSV and Asciidoc file format using Python script, enhancing the evaluation and reporting of model performance.
pytorchpythondeep-learningc-plus-plusmachine-learning
MicrosoftDocs/CNTK
Jan 2017 - Apr 2017
Contributions:29 commits in 2 months
pytorchdeep-learningcomputer-visionmachine-learningcognitive
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.