Crowdsourced delivery platforms operate as an intermediary between consumers who place orders and couriers who make deliveries; both of which are uncertain. The main challenge of a crowdsourced delivery platform is to meet a service level for their customers (e.g., 95% on-time delivery) by serving dynamically arriving orders with time windows. The two critical courier management decisions for a platform are how to schedule couriers and how to assign orders to couriers. These two decisions can be centralized (i.e., decided by the platform) or decentralized (i.e., decided by the couriers). Centralizing these decisions produces a more reliable workforce while decentralizing them may come with cost savings to the platform and allows for more freedom to couriers in deciding when and where to work. Crowdsourced delivery platforms have begun to utilize multiple courier types (i.e., a hybrid system) with the hope of reaping the advantages of each. In this paper, we address the challenge of managing two types of couriers at both the planning and operational level. We present fluid models for fleet sizing and order pricing that establish the superiority of a hybrid system over each system individually and we see total cost improvements ranging from 3-20% in a realistic example. Furthermore, we study order allocation in a hybrid system in depth, and propose order pooling and splitting policies. In our online experiments we find that a look ahead splitting policy outperforms pooling and batching policies by 2.4% while being more robust to uncertainty.