We propose a novel manufacturing-aware Topology Optimization algorithm for fiber-reinforced structures produced through Material EXtrusion Additive Manufacturing (MEX-AM). We consider short fibers suspended in a two-component thermosetting polymer. This avoids issues such as fiber discontinuity, folding and twisting, which are common with continuous fibers [1]. Recent research demonstrates that the microstructure can be controlled in deposited strands by modulating rotation of the printing nozzle during the deposition process [2]. The influence of multidirectional shearing induced by the rotating nozzle is investigated through a 3D Computational Fluid Dynamics model developed in OpenFOAM. It uses a moving overset mesh to model the nozzle depositing material into the background mesh. The volume of fluid method solves the multiphase flow and distinguishes the extruded reinforced polymer from the surrounding air, while fiber transport is modelled using the Advani-Tucker model where a second rank tensor describes the fiber orientation [3]. The effective material properties of the printed strand are related to the nozzle rotation speed, governing the strand’s anisotropy level. Using orientation averaging, a model is established showing that fibers align with the deposition direction when no rotation is applied whereas high rotation speed re-aligns fibers, resulting in almost isotropic properties. The material model is implemented with topology optimization to simultaneously optimize topology and material properties, using rotation speed as a design variable governing the local constitutive matrix. The algorithm is demonstrated on the MBB beam problem with simple, prescribed toolpaths of constant raster angle. Results show that no rotation should be applied in structural members aligning with the deposition direction, while rotation is beneficial to decrease the anisotropy level in unaligned members. This enables manufacturing components with the same topology but different microstructures, resulting in different responses to external stimuli and demonstrates the potential to tailor the local microstructure of 3D printed fiber-reinforced components using the MEX-AM process.