Synthetic Aperture Radar (SAR) systems are increasingly vulnerable to Radio Frequency Interference (RFI), creating a demand for advanced detection and mitigation strategies in complex operational environments. This paper introduces a novel framework for segmenting portions of the raw SAR data affected by RFI. Given the absence of publicly available SAR datasets with labels, spaceborne SAR data is simulated using SEMUS, an open-source RF-level SAR simulator, emulating Sentinel-1 raw data. This serves as the training data for a Deep Convolutional Neural Network (DCNN) optimized to segment the RFI portions. Performance is quantitatively validated against the classical energy detection method for interference detection. The robustness of the framework is further qualitatively tested on real Sentinel-1 SAR data in a zero-shot manner, demonstrating its capability to generalize to real-world scenarios despite the absence of real labeled data. This framework offers an adaptable solution for RFI detection in spaceborne SAR applications, contributing to improved data quality for Earth observation.