The challenge of navigating uneven terrain is a critical obstacle in the advancement of robotic locomotion. Traditional quadrupedal locomotion methods, such as walking, are often insufficient for dynamic and complex environments. Agile skills like jumping are necessary and must be adaptable over uneven terrain. This study addresses this issue by developing a policy for executing jumps over uneven terrain using a single demonstration. Initially, the system learns to imitate a forward jump based on a single demonstration from a SLIP trajectory planner. It then generalizes its jumping abilities to various distances in both forward and lateral directions. The study compares the performance of systems with and without parallel elasticity, demonstrating the energetic benefits of using elastic actuation for quadrupedal jumping. Results show that the system with parallel elastic actuation is 15.20% more energy-efficient and experiences a 15.79% reduction in peak power compared to the system without parallel elasticity. A policy trained using the proposed methodology successfully performs jumps of variable distances over uneven terrain with height perturbations of +/-4 cm using only proprioceptive information.