Achieving mastery in real-world software engineering tasks is fundamentally bottlenecked by the scarcity of large-scale, high-quality training data. Scaling such data has been limited by the complexity of environment setup, unit-test generation, and problem statement curation. In this paper, we propose Scale-SWE, an automated, sandboxed multi-agent workflow designed to construct high-quality SWE data at scale. The system coordinates three specialized agents—for environment setup, test creation, and problem description synthesis—to process 6 million pull requests across 5.2k repositories, producing Scale-SWE-Data: 100k verified SWE instances, the largest such dataset to date. It substantially surpasses existing real-world datasets in repository diversity and reflects realistic task complexity. We further demonstrate the dataset’s utility for training by distilling 71,498 high-quality trajectories and fine-tuning Qwen-30B-A3B-Instruct to produce Scale-SWE-Agent. Our agent achieves a 64% resolve rate on SWE-Bench-Verified—a nearly three-fold improvement over the base model. Scale-SWE provides a scalable, reproducible approach for data construction to advance LLM-based software engineering.