Janusz Marcinkiewicz is a software engineer with 11 years of experience, currently building backend systems at NVIDIA from Warsaw. He specializes in scalable storage and cloud-native infrastructure, contributing to high-profile open-source projects like NVIDIA/aistore and KubeVirt where he improved performance, stability, and build/config reliability. Comfortable across Go, Kubernetes, and AWS integrations, he has a track record of pragmatic refactors, logging and API fixes, and enhancing cloud bucket synchronization and downloaders in production systems. His background includes full academic training in computer science (BSc and MSc from the University of Warsaw) and early hands-on web work in Django and SEO that sharpened his product-focused mindset. Known for quietly reducing complexity in performance-sensitive code paths, he balances day-to-day engineering with thoughtful long-term maintainability. Based in Warsaw, he brings deep backend expertise to teams building large-scale AI and virtualization platforms.
11 years of coding experience
1 year of employment as a software developer
Warsaw School of Economics
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at University of Warsaw
Contributions:7 reviews, 1537 commits, 11 PRs in 4 years 6 months
Contributions summary:Janusz primarily focused on enhancing the functionality and stability of the AIStore backend system. Their contributions included bug fixes in the AWS object listing, refactoring code related to bucket checks and processing. Additionally, they added functionality related to cloud bucket synchronization and improvements to the downloader component. Their work also involved introducing performance improvements in several areas of the system.
Kubernetes Virtualization API and runtime in order to define and manage virtual machines.
Role in this project:
Back-end Developer / DevOps Engineer
Contributions:69 reviews, 23 commits, 26 PRs in 1 year 3 months
Contributions summary:Janusz primarily contributed to improving the efficiency and maintainability of the Kubevirt project. Their work included reducing logging in performance-sensitive code paths, refactoring logging-related components, and simplifying existing methods. They also addressed deprecated API usages and fixed flag defaults in the build configuration, along with propagating flags. The user demonstrated skills in Go, Kubernetes, and build system optimization.
kubernetesvirtual-machinevirtualizationlibvirtvms
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.