Abstract: Techniques are generally described for onboarding optimization for new hires. In various examples, labor order data specifying a target number of new hires may be received. In some examples, a first machine learning model may predict a number of new hire appointments to offer based at least in part on the labor order data. In further examples, an optimization component may allocate the number of new hire appointments to respective time slots based at least in part on candidate preferences. In some additional examples, data may be sent to a first application effective to display a list of available new hire appointment time slots of the respective time slots.

Abstract: Devices and techniques are generally described for scheduling optimization. In some examples, at least one processor may receive labor order data describing a plurality of work shifts and a respective number of candidates requested for each work shift of the plurality of work shifts. In various examples, an optimized set of schedules may be determined by solving an optimization problem based at least in part on the labor order data and a candidate preference signal representing a predicted popularity of proposed schedules for candidate workers. The optimized set of schedules may selected based on the respective number of candidates requested for each work shift and further based on historical candidate preferences. In at least some examples, first code may be generated that is effective to cause the optimized set of schedules to be displayed by a first computing device.