In February 2026, Balaji worked on enhancing reliability and reproducibility in machine learning workflows for the PriorLabs/tabpfn-extensions and PriorLabs/TabPFN repositories. He implemented deterministic behavior in the Random Forest model by standardizing random state usage, allowing users to specify a single seed for consistent results and simplifying the codebase. To address concurrency issues, he introduced a file locking mechanism in TabPFN, preventing race conditions during model downloads and ensuring data integrity under concurrent access. His work leveraged Python, concurrent programming, and software engineering best practices, demonstrating depth in backend development and a focus on robust, production-ready solutions.