Symbiotic job scheduling improves simultaneous multithreading (SMT) processor performance by coscheduling jobs that have "compatible" demands on the processor's shared resources. Existing approaches however require a sampling phase, evaluate a limited number of possible coschedules, use heuristics to gauge symbiosis, are rigid in their optimization target, and do not preserve system-level priorities/shares. This article proposes probabilistic job symbiosis modeling, which predicts whether jobs will create positive or negative symbiosis when coscheduled without requiring the coschedule to be evaluated. The model, which uses per-thread cycle stacks computed through a previously proposed cycle accounting architecture, is simple enough to be used in system software. Probabilistic job symbiosis modeling provides six key innovations over prior work in symbiotic job scheduling: (i) it does not require a sampling phase, (ii) it readjusts the job coschedule continuously, (iii) it evaluates a large number of possible coschedules at very low overhead, (iv) it is not driven by heuristics, (v) it can optimize a performance target of interest (e. g., system throughput or job turnaround time), and (vi) it preserves system-level priorities/shares. These innovations make symbiotic job scheduling both practical and effective. Our experimental evaluation, which assumes a realistic scenario in which jobs come and go, reports an average 16% (and up to 35%) reduction in job turnaround time compared to the previously proposed SOS (sample, optimize, symbios) approach for a two-thread SMT processor, and an average 19% (and up to 45%) reduction in job turnaround time for a four-thread SMT processor.