Yes, you are on the right track! I encourage you to go look at the PyTorch documentation to better understand what you are doing. You’ll find that the second argument to nn.Linear is the output dimension. out_features. You have implemented this in such a way that it gets the default values from config. Instead, you can change those output features as you wish. E.g.
self.classifier_job_type = nn.Linear(config.hidden_size, 5)
self.classifier_edu_level = nn.Linear(config.hidden_size, 3)

It may seem from that printed model that those classification layers are sequential in the model, but do not worry, that is not the case. It simply represents the order in which you defined the layers. The “actual” data flow order is controlled by the forward method, that you still have to implement. It should go like this (in wors, so that you can implement it yourself):

Push the encoded inputs through self.bert
Push the output of self.bert through a dropout and then through the first classification layer
Push the output of self.bert through a dropout and then through the second classification layer
Calculate loss of output first classification and of output in second classification layer and then sum the loss
During training you then can do a single backward pass