Worked on the timholy/boltz repository to enhance the stability of its core featurization pipeline by addressing tensor size inconsistencies in the Featurizer module. Using Python and leveraging skills in data processing and tensor manipulation, implemented a padding strategy for the cyclic_period tensor to enforce a static size during forward passes. This approach eliminated a class of runtime dimension errors, improving reliability across both training and inference workflows. The solution was integrated with existing regression tests to ensure ongoing stability and included updated documentation to clarify tensor sizing rules, supporting the repository’s focus on production readiness and reproducibility.