Worked on the NVIDIA/NeMo repository to improve the reliability of experiment tracking by addressing issues with exporting model configuration parameters to Weights & Biases. Focused on ensuring that the _hparams_initial attribute was fully JSON serializable, which is essential for robust hyperparameter logging and reproducibility across machine learning runs. Developed a dedicated test helper to validate serializability and prevent future regressions, reinforcing the export workflow for configuration management. Utilized Python and applied skills in deep learning, model training, and testing to stabilize the process of saving and restoring model configurations, ultimately reducing metadata drift and supporting consistent experiment documentation.