Ocean deoxygenation poses major challenges to marine life and can alter carbon cycling. Direct measurements of dissolved oxygen are sparse, and interpolation methods are needed to study the variability and changes in oxygen content. In this work, we used machine learning to improve estimates of oxygen levels across the global ocean. Our approach produces a new gridded product that captures detailed changes in oxygen over time and space.