Phenomena responsible for emerging technologies originate from novel molecular states at buried interfaces. Yet quantitatively resolving these features is out of reach in most cases. Resonant scattering has demonstrated sensitivity to these local states but is limited due to a lack of accurate interpretive models. We leverage a machine learning (ML)-optimized optical model to analyze polarized resonant soft X-ray scattering (RSoXS) from molecular thin films. This new analysis establishes the direct and quantitative interpretation of molecular orientation, chemical identity and packing states as a function of depth with sub-monolayer resolution - reaching beyond the diffraction limit via locking energy and polarization through the optical model. From our analysis, we discover a gradient evolution of molecular orientation and packing emerging from the interface, indicative of competing surface versus bulk growth dynamics and contradicting hypotheses of substrate patterning. This nondestructive insight into buried interfacial formation and final state opens possibilities in controlling interfacial states and phenomena in next-generation technologies.