Correspondence studies have found evidence of age discrimination in callback rates for older workers, but less is known about whether job advertisements can themselves shape the age composition of the applicant pool. We construct job ads for administrative assistant, retail, and security guard jobs, using language from real job ads collected in a prior large-scale correspondence study (Neumark et al., 2019a). We modify the job-ad language to randomly vary whether the job ad includes ageist language regarding age-related stereotypes. Our main analysis relies on computational linguistics/machine learning methods to design job ads based on the semantic similarity between phrases in job ads and age-related stereotypes. In contrast to a correspondence study in which job searchers are artificial and researchers study the responses of real employers, in our research the job ads are artificial and we study the responses of real job searchers.