Shore-based maritime surveillance radars suffer from systematic range and azimuth errors that degrade target-tracking accuracy. This paper proposes a Vision Transformer (ViT) variant that corrects these errors using Automatic Identification System (AIS) data as the ground truth, modelling nonlinear error patterns via self-attention without requiring explicit physical models of the underlying error sources. Evaluated on the Maritime Target Detection and Tracking (MTDSP) dataset (≈80,000 paired radar-AIS observations), the proposed method reduces range mean absolute error (MAE) by 98.5% (514.76 m → 7.77 m) and azimuth MAE by 89.8% (1.37° → 0.14°) relative to uncalibrated measurements. Controlled experiments isolating architectural components confirm that self-attention, patch embedding, and multi-task learning each contribute measurable gains, particularly in tail-error robustness. These results demonstrate the viability of Transformer-based architectures for correcting radar systematic errors in maritime surveillance.