Molecular conformer generation is vital to computational chemistry and drug discovery, but it remains challenging due to the extensive range of possible conformations. In this paper, we propose a novel approach for molecular conformer generation that utilizes an Equivariant Transformer Forcefield (ETF) pre-trained on large-scale molecular datasets to refine the quality of the conformers. This strategy begins with an initial set of conformers, which are subsequently refined through structural optimization. We demonstrate that our ETF-based optimization significantly improves the quality of the conformers generated by state-of-the-art methods, achieving a 45% reduction in the distance to the reference conformers. Furthermore, our methodology outperforms classical forcefields by improving precision without sacrificing recall. Lastly, it can deliver competitive performance even when beginning with a simple initialization of conformers by RDKit, demonstrating its robustness and potential for extensive applications in computational chemistry and drug discovery.