This paper tests for employer learning about worker ability and quantifies the impact of learning on job mobility and innovation output in the labor market for computer scientists. I match the job history of over 40,000 Ph.D. computer scientists (CS) with publications and patent applications that signal their research ability. Workers who publish at CS conferences are 45% more likely to move into a top tech firm in the next year than similar coworkers without such a public signal. Higher-quality papers are often filed as patent applications, but the fact of filing remains private information at the incumbent employer for 18 months. Authors of such papers experience a delayed increase in upward mobility. Without employer learning from the public research records, innovation output by early career computer scientists would drop by 15% due to greater misallocation of talent. Reducing asymmetric information between employers can expedite positive assortative matching and increase innovation by 5%.