Worked on enhancing distributed data handling in the google/flax repository by delivering a feature that preserves sharding metadata across distributed operations. Focused on improving the reliability and scalability of large-scale training workflows, the approach involved introducing a build_shaped_array helper to maintain sharding information within axes_scan and removing an unnecessary jnp.array call in normalization.py to ensure consistent metadata propagation. Leveraged expertise in distributed systems, JAX, and machine learning, with all development carried out in Python. The work emphasized robustness and correctness, aligning with project standards and contributing to more reliable sharded computations without introducing new bugs during the period.