We demonstrate FaiRank, an interactive system to explore fairness of ranking in online job marketplaces. FaiRank takes as input a set of individuals and their attributes, some of which are protected, and a scoring function, through which those individuals are ranked for jobs. It finds a partitioning of individuals on their protected attributes over which fairness of the scoring function is quantified. FaiRank has several appealing features: (1) It can be used by different users: the auditor whose role is to monitor the fairness of ranking in a job marketplace, the job owner seeking to examine the influence of a scoring function and its variants on the ranking of candidates for a job, and the end-user who wants to assess the fairness of jobs on different marketplaces; (2) It is able to quantify fairness under different data and process transparency settings: when some attributes are anonymized and when only the ranking (and not the scoring function) is available; (3) It is interactive and lets its users explore different scoring functions and examine how fairness evolves; (4) It is generic and provides the ability to quantify different notions of fairness. Our demonstration will provide attendees with several scenarios for fairness of ranking in job marketplaces to experiment with and acquire an understanding of this important research question and its impact in practice.