This chapter covers the implemented placement engines. First, a reinforcement learning (RL) based approach will be described, followed by a simulated annealing (SA) one. For the RL engine, first the environment-agent interface and the definitions of the rewards, states, and actions will be presented. This is followed by the implementation details of the environment and agent, which include the used RL algorithm and neural network. For the SA engine, the mapping of the placement problem to a rectangle packing problem and the optimization of it by using a Python library will be shown. To find the placement of a hierarchical circuit, a bottom-up approach will be discussed. At the end of this chapter, a legalization process for fixing non-design-rule-compliant placements will be described that is based on a cell sliding strategy.