Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers with information directly extracted from resumes and vacancies. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual search-based rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a deep learning solution with rich features including three configurable modules that can be plugged with little restrictions. Namely, unsurprised feature extraction, base classifiers training and ensemble method learning. The major contributions of our work are divided into three aspects. Rather than using manual rules, ma- chine learned methods to automatically detected the semantic similarity of positions are proposed. Then several competitive “shallow” estimators and “deep” estimators are selected. Finally, an ensemble algorithm to bag these estimators and aggregate their individual predictions to form a final prediction is verified. Experimental results over 47 thousand resumes show that our solution can significantly improve the predication precision of job matching, including current position, salary, educational background (detect abnormal candidates who may fake their background or may be really excellent) and company scale.