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Abstract Thin film evaporation is a widely-used thermal management solution for micro/nano-devices with high energy densities. Local measurements of the evaporation rate at a liquid-vapor interface, however, are limited. We present a continuous profile of the evaporation heat transfer coefficient ( $$h_{\text {evap}}$$ h evap ) in the submicron thin film region of a water meniscus obtained through local measurements interpreted by a machine learned surrogate of the physical system. Frequency domain thermoreflectance (FDTR), a non-contact laser-based method with micrometer lateral resolution, is used to induce and measure the meniscus evaporation. A neural network is then trained using finite element simulations to extract the $$h_{\text {evap}}$$ h evap profile from the FDTR data. For a substrate superheat of 20 K, the maximum $$h_{\text {evap}}$$ h evap is $$1.0_{-0.3}^{+0.5}$$ 1 . 0 - 0.3 + 0.5  MW/ $$\text {m}^2$$ m 2 -K at a film thickness of $$15_{-3}^{+29}$$ 15 - 3 + 29  nm. This ultrahigh $$h_{\text {evap}}$$ h evap value is two orders of magnitude larger than the heat transfer coefficient for single-phase forced convection or evaporation from a bulk liquid. Under the assumption of constant wall temperature, our profiles of $$h_{\text {evap}}$$ h evap and meniscus thickness suggest that 62% of the heat transfer comes from the region lying 0.1–1 μm from the meniscus edge, whereas just 29% comes from the next 100 μm.