LinkedIn is the largest social network for professionals where users of the service share all of their professional history. In this work we explore methods by which we can model the career trajectory of a given candidate and predict future career moves. First, we attempted to normalize the job titles data as we have found that there is a great deal of variation in how the users of the professional social networking platform decide to input their titles. Then we move on to exploring various predictive models inspired form language models as well as sequential neuronal models.