1. A method for clustering annotators via a distributed data annotation process, comprising: obtaining a set of source data using a distributed data annotation server system, where a piece of source data in the set of source data comprises at least one identifying feature and the source data comprises image data; determining a training data set representative of the set of source data using the distributed data annotation server system, where at least one piece of source data in the training data set comprises source data metadata describing the ground truth for the piece of source data, where the ground truth for a piece of source data describes at least one feature contained in the piece of source data and a correct label associated with at least one feature; obtaining at least one set of annotations from a plurality of annotators for a portion of the training data set using the distributed data annotation server system, where an annotation identifies one or more features within a piece of source data in the training data set; for each annotator: determining annotator recall metadata based on the set of annotations provided by the annotator for the training data set using the distributed data annotation server system, where the annotator recall metadata comprises a measure of the number of features within a piece of source data identified with a label in the set of annotations by the annotator; and determining annotator precision metadata based on the set of annotations provided by the annotator for the training data set using the distributed data annotation server system, where the annotator precision metadata comprises a measure of the number of correct annotations associated with each piece of source data based on the ground truth for each piece of source data; and grouping the annotators into annotator groups based on the annotator recall metadata and the annotator precision metadata using the distributed data annotation server system.