  Person-job fit is an essential part of online recruitment platforms in
serving various downstream applications like Job Search and Candidate
Recommendation. Recently, pretrained large language models have further
enhanced the effectiveness by leveraging richer textual information in user
profiles and job descriptions apart from user behavior features and job
metadata. However, the general domain-oriented design struggles to capture the
unique structural information within user profiles and job descriptions,
leading to a loss of latent semantic correlations. We propose TAROT, a
hierarchical multitask co-pretraining framework, to better utilize structural
and semantic information for informative text embeddings. TAROT targets
semi-structured text in profiles and jobs, and it is co-pretained with
multi-grained pretraining tasks to constrain the acquired semantic information
at each level. Experiments on a real-world LinkedIn dataset show significant
performance improvements, proving its effectiveness in person-job fit tasks.
