Network embedding has been increasingly employed in networked data mining applications as it is effective to learn node embeddings that encode the network structure. Existing network models usually learn a single embedding for each node. In practice, a person may interact with others in different roles, such as interacting with schoolmates as a student, and with colleagues as an employee. Obviously, different roles exhibit different characteristics or features. Hence, only learning a single embedding responsible for all roles is not appropriate. In this paper, we thus introduce a concept of multiple social role (MSR) into social network embedding for the first time. The MSR models multiple roles people play in society, such as student and employee. To make the embedding more versatile, we thus propose a multiple social role embedding (MSRE) model to preserve both the network structure and social roles. Empirical evaluation on various real-world social networks demonstrates advantages of the proposed MSRE over the state-of-the-art embedding models in link prediction and multi-label classification.