Mikhail Pravilov
Software Engineer at Google
Munich, Bavaria, Germany
Summary
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RockstarMikhail Pravilov is a software engineer based in Munich with nine years of experience, currently working at Google. He specializes in back-end and systems programming, with hands-on expertise in multi-threaded C++ development for high-performance computing. His open-source work on an HPC course repository highlights deep familiarity with concurrency primitives—mutexes, condition variables, barriers—and practical debugging of race conditions and error handling. He also implemented thread-safe random number generation and interruptible workflows, showing attention to both correctness and robustness. Colleagues would find him pragmatic and detail-oriented, able to translate complex synchronization requirements into reliable production code. Fluent in the demands of low-level performance engineering, he brings a tested combination of systems thinking and executable results.
9 years of coding experience
Stackoverflow
55reputation
2kreached
0answers
6questions
Github Skills (19)
c-language10
multithreading10
mutex10
pthread10
mux10
condition-variable10
cprogramming-language10
concurrency10
pthreads10
error-handling9
algorithms8
algorithm8
audio6
android6
photoeditor-sdk6
Programming languages (8)
Github contributions (5)
eugenyk/hpcourse
Mar 2019 - May 2019
Repository to store student's practical works on high performance computing course
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 4 PRs, 2 comments in 2 months
Contributions summary:Mikhail primarily focused on developing and debugging multi-threaded applications in C++ for a high-performance computing course. They implemented a producer-consumer problem using mutexes, condition variables, and barriers. Their work included adding error checking and addressing concurrency issues to achieve correct results. The user also introduced thread-safe random number generation and implemented interrupt functionality in the application.
high-performance-computingpracticalperformancecomputingpractical-works
ml-in-programming/ml-on-source-code-models
Feb 2019 - Jun 2019
Repository of models reproduced by papers without open source implementation. Papers topic is machine learning on source code.
Contributions:131 commits, 2 PRs, 114 pushes in 3 months
deep-learningmachine-learningmachine-learning-on-source-codereproduced
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