Unlike many qubit technologies, a gate-defined silicon spin qubit is not simply fabricated and then calibrated: electrostatic tuning creates the quantum dots, sets their electron occupation and couplings, and ultimately defines the qubit itself. This makes autotuning unusually demanding, but also highly structured and local—opening the possibility that machines need not reproduce the measurements or strategies used by human experimentalists, and that many qubit cells can ultimately be tuned in parallel. I will present our modular closed-loop autotuning framework and results on autonomous double-quantum-dot tuning, including vision models reaching ~94% overall accuracy and ~98% transition-counting accuracy on clean charge-stability maps, at least 50% autonomous charge-state-tuning success on previously unseen devices, and transfer to a different refrigerator and unseen device where requested two- and four-electron configurations were reached in under 30 minutes. Finally, I will show how exploiting the strong structure—and hence compressibility—of these measurements can reduce charge-stability-map acquisition by up to 64×, pointing toward machine-native, massively parallel calibration.