Inspired by developments in machine learning, specifically in the field of automatic differentiation, we present a new software called JAX-ReaxFF that enables extremely fast optimization of Reax force field parameters . JAX is an auto-differentitation software by Google for high performance machine learning research , it can automatically differentiate native Python and NumPy functions. Leveraging this capability, JAX-ReaxFF automatically calculates the derivative of a given fitness function, which essentially measures the root mean squared deviation (RMSD) of a force field against a reference dataset, from Python-based implementation of the ReaxFF potential energy terms with respect to the set of force field parameters to be optimized. By learning the gradient information of the high dimensional optimization space (which generally includes tens to over a hundred parameters), JAX-ReaxFF can employ the highly effective local optimization methods such as the Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm and Sequential Least Squares Programming (SLSQP) optimizer. The gradient information alone is obviously not sufficient to prevent the local optimizers from getting stuck in a local minima, but when combined with a multi-start approach, JAX-ReaxFF can greatly improve the training efficiency (measured in terms of the number of fitness function evaluations performed) and significantly reduce the time spent on optimizing ReaxFF parameters.