Most large-scale online recommender systems like newsfeed ranking, people recommendations, job recommendations, etc. often have multiple utilities or metrics that need to be simultaneously optimized. The machine learning models that are trained to optimize a single utility are combined together through parameters to generate the final ranking function. These combination parameters drive business metrics. Finding the right choice of the parameters is often done through online A/B experimentation, which can be incredibly complex and time-consuming, especially considering the non-linear effects of these parameters on the metrics of interest. In this tutorial, we will talk about how we can apply Bayesian Optimization techniques to obtain the parameters for such complex online systems in order to balance the competing metrics. First, we will provide an in-depth introduction to Bayesian Optimization, covering some of the basics as well as the recent advances in the field. Second, we will talk about how to formulate a real-world recommender system problem as a black-box optimization problem that can be solved via Bayesian Optimization. We will focus on a few key problems such as newsfeed ranking, people recommendations, job recommendations, etc. Third, we will talk about the architecture of the solution and how we are able to deploy it for large-scale systems. Finally, we will discuss the extensions and some of the future directions in this domain.