Iris recognition is a reliable personal identification method but there is still much room to improve its accu- racy especially in less-constrained situations. For example, free movement of head pose may cause large rotation dif- ference between iris images. And illumination variations may cause irregular distortion of iris texture. To match intra-class iris images with head rotation robustly, the exist- ing solutions usually need a precise alignment operation by exhaustive search within a determined range in iris image preprosessing or brute-force searching the minimum Ham- ming distance in iris feature matching. In the wild envi- roments, iris rotation is of much greater uncertainty than that in constrained situations and exhaustive search within a determined range is impracticable. This paper presents a unified feature-level solution to both alignment free and distortion robust iris recognition in the wild. A new deep learning based method named Alignment Free Iris Net- work (AFINet) is proposed, which utilizes a trainable VLAD (Vector of Locally Aggregated Descriptors) encoder called NetVLAD [18] to decouple the correlations between local representations and their spatial positions. And deformable convolution [5] is leveraged to overcome iris texture distor- tion by dense adaptive sampling. The results of extensive experiments on three public iris image databases and the simulated degradation databases show that AFINet signifi- cantly outperforms state-of-art iris recognition methods.