Welfare-Optimal Matching in Hiring Markets via Min-Cost Flow with Ensemble Slot Allocation

We formulate the talent-to-job matching problem as a min-cost max-flow optimization with tiered job nodes and ensemble slot allocation. MCF-Tiered achieves 95.3% company satisfaction versus 24.5% for greedy per-job ranking (p < 10^-20), validated across three independent datasets with adaptive premium bonus for scale-invariant equity distribution.