Kiarash Majdi
Full Stack AI Developer | Systems Research Specialist
Building intelligent systems at the intersection of machine learning, distributed systems, and security.
About
I am a full stack AI developer and systems researcher specializing in machine learning, distributed systems, and security. Currently pursuing a Master's in Computer Science at the University of Waterloo, my work focuses on data-driven approaches to low-level systems security, including distributed systems, file systems, version control, networking, and operating systems.
I am open to part-time remote or Waterloo-based opportunities in Machine Learning Engineering, Data Engineering, Security Analysis, Infrastructure Engineering, and Software Engineering roles.
Experience
Co-leading systems research on next-generation enterprise version control architectures that remain performant under extreme developer concurrency, where traditional monolithic VCS deployments become bottlenecked and materially impact developer productivity. Drove the design of an initial revision data model and an end-to-end ingestion pipeline to convert and normalize large-scale Git and Subversion histories into a custom format suitable for controlled experimentation across real-world repositories. Built early clone/checkout functionality in C++ and Python-based data collection and conversion tooling, establishing a working prototype that supports repeatable evaluation. Defined the initial benchmarking approach around repository footprint and latency of developer-critical workflows (e.g., checkout/clone time), using results to guide ongoing exploration of filesystem layout choices and optimization paths for scalability and performance.
Architected and deployed multi-agent AI systems using cutting-edge LLM technologies. Built production-ready agentic workflows for complex automation tasks.
Researched and implemented machine learning models for security anomaly detection. Published findings on adversarial robustness in production AI systems.
Conducted research on distributed storage systems and RAID optimization. Developed novel approaches to improve storage performance and reliability in cloud environments.