This paper presents a human-in-the-loop approach to address the challenge of low-resource neural machine translation (NMT), focusing on the Tibetan-Chinese language pair. We emphasize the crucial role of human feedback in both data augmentation and model optimization. First, we construct a large-scale Tibetan-Chinese parallel corpus by iteratively leveraging back-translation and incorporating human evaluation to guide the generation of high-quality synthetic data. Then, we train a multilingual NMT system using a curriculum learning strategy, progressively incorporating the augmented data. Finally, we fine-tune our model with GaLore and SimPO algorithms, directly optimizing it towards human preferences as assessed by professional translators. Experimental results on the CCMT 2024 Tibetan-Chinese translation task demonstrate that our approach significantly improves translation quality, achieving state-of-the-art performance. We provide further analysis and case studies to illustrate the effectiveness of our human-in-the-loop methodology.