In this talk, we introduce a Reformulation-Linearization Technique-based open-source optimization software for solving polynomial programming problems (RLT-POS). We present algorithms and mechanisms that form the backbone of RLT-POS, including constraint filtering techniques, reduced RLT representations, semidefinite cuts, and bound-grid-factor constraints. When implemented individually, each model enhancement has been shown to significantly improve the performance of the standard RLT procedure. However, coordination between model enhancement techniques becomes critical for an improved overall performance since special structures in the original formulation may be lost after implementing a particular model enhancement. More specifically, we discuss the coordination between 1) constraint elimination via filtering techniques and reduced RLT representations, and 2) semidefinite cuts and bound-grid-factor constraints. We present computational results using instances from the literature as well as randomly generated problems to demonstrate the improvement over standard RLT, and compare the performances of the software packages BARON, SparsePOP, and Couenne with RLT-POS.