Kirill focused on backend stability and distributed systems reliability, addressing two critical bugs in the huggingface/trl and linkedin/Liger-Kernel repositories. Using Python and leveraging expertise in API integration and code refactoring, Kirill improved distributed training by ensuring the vLLM client initializes only on the main process in server mode, preventing failures during distributed runs. In Liger-Kernel, Kirill restored missing low-level API imports, aligning actual functionality with documented features. These targeted fixes enhanced code maintainability and robustness across distributed environments. The work demonstrated a thoughtful approach to edge-case handling and contributed to more predictable, reliable behavior in complex machine learning pipelines.