  As the world's largest professional network, LinkedIn wants to create
economic opportunity for everyone in the global workforce. One of its most
critical missions is matching jobs with processionals. Improving job targeting
accuracy and hire efficiency align with LinkedIn's Member First Motto. To
achieve those goals, we need to understand unstructured job postings with noisy
information. We applied deep transfer learning to create domain-specific job
understanding models. After this, jobs are represented by professional
entities, including titles, skills, companies, and assessment questions. To
continuously improve LinkedIn's job understanding ability, we designed an
expert feedback loop where we integrated job understanding models into
LinkedIn's products to collect job posters' feedback. In this demonstration, we
present LinkedIn's job posting flow and demonstrate how the integrated deep job
understanding work improves job posters' satisfaction and provides significant
metric lifts in LinkedIn's job recommendation system.
