Simulations and physical experiments are often resource intensive, and may take days or weeks to obtain estimates of a particular design under consideration. Bayesian optimization is a popular approach to solving such problems. I will discuss recent advances in Bayesian optimization and open source software developed at Meta to solve complex scientific and engineering problems, including methods for high-dimensional optimization, combinatorial, multi and many-objective optimization, and targeting long-term outcomes with short-term experiments. I will present these methods in the context of applications to inverse design of optical components, concrete formulations, and machine learning systems.