Current structural design codes (e.g., Eurocodes, NTC 2018) adopt a component-based reliability format, where each member satisfies target reliability indices for specific limit states. However, structural system reliability is governed by ductility, load redistribution, statistical dependence, and the variability of loads and resistances. Consequently, a code paradox may arise; the system fails to achieve the target reliability even when all components satisfy their prescribed targets [1]. The inconsistency is especially evident in brittle and/or series-like systems, where failure of a critical component can trigger cascading collapse (i.e., a system failure) [2]. To maintain compatibility with component-level verification while enforcing a target system reliability, the redundancy factor, ηR, is introduced [3]. ηR is defined as the minimum resistance-level multiplier so that the system attains a target reliability index. The calibration is formulated as an inverse reliability problem, predominantly driven by tail probabilities. The crude Monte Carlo requires prohibitively large samples, while still providing poor resolution. Therefore, we adopt Hamiltonian Monte Carlo-based subset simulation [4-5] to obtain stable estimates of ηR. The Daniels system is used as a benchmark [6-7] to capture trends of ηR across different governing parameters (e.g., variability of load and resistance, ductility, number of components and statistical dependences). Despite various distributions of loads and resistances or correlations, the calibrated values shift modestly but the overall trend remained consistent. While the ideal series and parallel systems, respectively, provide the upper and lower bounds on ηR, mixed systems behave nearly identically to single components. It is notable that series-like brittle systems abruptly shift towards ductile-like behavior with minor increases in component ductility or resistance variability. It provides a robust theoretical and computational basis for integrating system-level safety targets into future design standards through advanced simulation methods.