Modern recruitment platforms operate under severe information imbalance: job seekers must search over massive, rapidly changing collections of postings, while employers are overwhelmed by high-volume, low-relevance applicant pools. Existing recruitment recommender systems typically rely on keyword matching or single-stage semantic retrieval, which struggle to capture fine-grained alignment between candidate experience and job requirements under real-world scale and cost constraints. We present Synapse, a multi-stage semantic recruitment system that separates high-recall candidate generation from high-precision semantic reranking, combining efficient dense retrieval using FAISS with an ensemble of contrastive learning and Large Language Model (LLM) reasoning. To improve transparency, Synapse incorporates a retrieval-augmented explanation layer that grounds recommendations in explicit evidence. Beyond retrieval, we introduce a novel evolutionary resume optimization framework that treats resume refinement as a black-box optimization problem. Using Differential Evolution with LLM-guided mutation operators, the system iteratively modifies candidate representations to improve alignment with screening objectives, without any labeled data. Evaluation shows that the proposed ensemble improves nDCG@10 by 22% over embedding-only retrieval baselines, while the evolutionary optimization loop consistently yields monotonic improvements in recommender scores, exceeding 60% relative gain across evaluated profiles. We plan to release code and data upon publication.

翻译：现代招聘平台在严重的信息不对称环境下运行：求职者需从海量且快速变化的职位发布中进行搜索，而雇主则被高数量低相关性的应聘者池所淹没。现有招聘推荐系统通常依赖关键词匹配或单阶段语义检索，难以在实际规模与成本约束下捕捉候选者经验与职位需求之间的精细对齐。我们提出Synapse——一种多阶段语义招聘系统，将高召回率候选人生成与高精度语义重排序分离，结合基于FAISS的高效稠密检索与对比学习及大语言模型推理的集成方法。为提升透明度，Synapse引入检索增强的解释层，将推荐锚定于显式证据。在检索之外，我们提出一种新型进化简历优化框架，将简历优化视为黑盒优化问题。该系统采用大语言模型引导变异算子的差分进化算法，无需任何标注数据即可迭代修正候选者表征以提升与筛选目标的对齐度。评估表明，所提集成方法在nDCG@10上较纯嵌入检索基线提升22%，而进化优化循环在推荐分数上持续呈现单调提升，在评估画像上相对增益超过60%。我们计划在论文发表后公开代码与数据。