Alexander Malyshev
Software Engineer at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolAlexander Malyshev is a software engineer based in New York with 12 years of experience building high-performance backend systems at Meta (Facebook). He focuses on low-level runtime optimization and code efficiency, contributing to Meta’s Cinder project — a production-grade, performance-oriented fork of CPython — where he improved copy propagation, boxing/unboxing, and reduced code duplication with X Macros. His background includes hands-on teaching and course assistance at Carnegie Mellon University, reflecting strong fundamentals and an ability to communicate complex concepts. Alexander combines production-scale engineering at a top-tier tech company with deep systems-level knowledge of interpreters and compilers. He’s comfortable digging into bit-level and code-generation details while delivering pragmatic fixes that measurably improve runtime behavior. Notably, his open-source contributions reveal a bias for measurable performance gains rather than cosmetic changes.
13 years of coding experience
Bachelor of Science (B.S.), Computer Science, Bachelor of Science (B.S.), Computer Science at Carnegie Mellon University
English
Github Skills (14)
compiler10
compiler-compiler10
runtimes10
jit10
interpreter10
compile10
optimization10
python9
cprogramming-language9
c-language9
algorithms8
data-structures8
algorithm8
data-structure8
Programming languages (5)
Github contributions (5)
facebookincubator/MetaPython
Jun 2022 - Jan 2023
This is Meta's fork of the CPython runtime. The name "cinder" here is historical, see https://github.com/facebookincubator/cinderx for the Python extension / JIT compiler.
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:50 commits, 3 PRs, 9 pushes in 7 months
Contributions summary:Alexander primarily focused on optimizing the performance of the Cinder Python runtime. Their contributions included creating helper functions for copy propagation, fixing bugs in the `Select` LIR instruction, and simplifying unary operations for boolean types. They also implemented improvements related to boxing and unboxing of primitive types and refined code generation by leveraging X Macros for enum definitions, reducing code duplication and improving clarity. These changes demonstrate a focus on low-level optimization and code efficiency within the CPython runtime environment.
compilercpythonjitpython-extensioninterpreter
alexmalyshev/emacs.d
Jan 2018 - Aug 2025
Contributions:22 pushes, 1 branch in 7 years 8 months
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