Deep Job Understanding at Linkedln

As the world's largest professional network, Linkedln 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 Linkedln'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 Linkedln's job understanding ability, we designed an expert feedback loop where we integrated job understanding models into Linkedln's products to collect job posters' feedback. In this demonstration, we present Linkedln's job posting flow and demonstrate how the integrated deep job understanding work improves job posters' satisfaction and provides significant metric lifts in Linkedln's job recommendation system.