Blind and low-vision (BLV) individuals face high unemployment rates. The job search is becoming harder as more employers use AI-driven systems to screen resumes before a human ever sees them. Such AI systems could inadvertently further disadvantage BLV job seekers, introducing additional barriers to an already difficult process. We lack understanding of BLV job seekers' experiences in today's AI-driven hiring ecosystem. Without such understanding, we risk designing technologies that create new systemic barriers for BLV job seekers rather than providing support. To this end, we conducted interviews with 17 BLV job seekers and analyzed their experiences with AI-powered hiring systems. We found that AI hiring systems misrepresented their professional identities and created dehumanizing interactions. To level the playing field, BLV job seekers used strategic counter-navigation: they deployed their own tools to bypass algorithmic screening and built peer networks to share AI literacy. They also practiced 'strategic refusal', choosing to avoid certain AI systems to regain their agency. Unlike prior work that frames job search as an individualistic activity, or one focused on being compliant with employer needs, we use the interdependence framework to argue that for BLV people, job search is an interdependent process. We offer design recommendations for AI-mediated tools that center disability perspectives and support interdependencies in job search.

翻译：视障及低视力（BLV）群体面临高失业率。随着越来越多雇主在简历进入人工审核前采用AI驱动系统进行筛选，求职难度日益增加。这类AI系统可能无意间进一步使BLV求职者处于不利地位，为本已艰难的流程增设障碍。我们目前缺乏对BLV求职者在当今AI驱动的招聘生态中体验的理解。若缺乏这种认知，我们可能设计出为BLV求职者制造系统性新障碍而非提供支持的技术。为此，我们访谈了17位BLV求职者，分析他们与AI辅助招聘系统的互动经历。研究发现，AI招聘系统曲解其职业身份，并造成非人性化的交互体验。为争取公平竞争，BLV求职者采取策略性逆向导航：自主开发工具绕过算法筛选，构建同行网络共享AI素养。他们还践行"战略性拒绝"，选择规避某些AI系统以重获自主权。不同于将求职视为个体化活动或聚焦顺从雇主需求的既有研究，我们借助相互依存框架论证：对BLV群体而言，求职本质上是相互依存的过程。我们提出以残障视角为核心、支持求职过程中相互依存关系的AI中介工具设计建议。