Post-acute infection syndromes (PAIS) may develop after acute viral disease1. Infection with SARS-CoV-2 can result in the development of a PAIS known as “Long COVID” (LC). Individuals with LC frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions2–4; however, the biological processes associated with the development and persistence of these symptoms are unclear. Here, 273 individuals with or without LC were enrolled in a cross-sectional study that included multi-dimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with LC. Marked differences were noted in circulating myeloid and lymphocyte populations relative to matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with LC. Further, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with LC, particularly Epstein-Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with LC. Integration of immune phenotyping data into unbiased machine learning models identified key features most strongly associated with LC status. Collectively, these findings may help guide future studies into the pathobiology of LC and aid in developing relevant biomarkers.