Symbiotic job scheduling boosts simultaneous multithreading (SMT) processor performance by co-scheduling jobs that have 'compatible' demands on the processor's shared resources. Existing approaches however require a sampling phase, evaluate a limited number of possible co-schedules, use heuristics to gauge symbiosis, are rigid in their optimization target, and do not preserve system-level priorities/shares. This paper proposes probabilistic job symbiosis modeling, which predicts whether jobs will create positive or negative symbiosis when co-scheduled without requiring the co-schedule 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 co-schedule continuously, (iii) it evaluates a large number of possible co-schedules 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.