We introduce a new system for Multi-Session SLAM, which tracks camera motion across multiple disjoint videos under a single global reference. Our approach couples the prediction of optical flow with solver layers to estimate camera pose. It is trained end-to-end using a novel differentiable solver for wide-baseline two-view pose. The full system can connect disjoint sequences, perform visual odometry, and efficient global-optimization/loop-closure. Compared to other Multi-Session SLAM approaches, our design is more accurate and robust to catastrophic failures.