We are developing machine learning-driven autonomous metrology research systems, with the goal of accelerating the development of self-correcting photonic and quantum sensor networks. These systems combine machine learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration of prior knowledge into the data analysis, including both device physics and material properties.We are primarily interested in photonic (e.g. silicon ring resonators) and quantum (NV diamond) sensor networks for thermodynamic metrology (temperature, pressure and humidity).