This document presents DexMachina, a novel curriculum-based reinforcement learning algorithm for functional retargeting in bimanual dexterous manipulation. The method focuses on teaching robot hands to replicate human object manipulation trajectories from demonstrations, particularly for articulated objects and complex, long-horizon tasks. By employing virtual object controllers that gradually reduce their influence and providing auxiliary rewardsbased on motion and contact, DexMachina allows the robot policy to learn and adapt to the unique capabilities of different dexterous hands. The paper also introduces a simulation benchmark for evaluating both functional retargeting algorithms and various dexterous hand designs, demonstrating DexMachina's superior performance compared to baseline methods across a range of hands and tasks.