Machine learning is increasingly being used to help tackle climate change, from optimizing electrical grids to emulating climate models and monitoring biodiversity. As such applications grow, however, it is becoming clear that high-powered ML tools often fall short. Methods designed using standard benchmarks may fail to capture the constraints or metrics of real-world problems, while a “one size fits all” approach ignores useful auxiliary information in specific applications. In this talk, we show how problem-centered design can lead to ML algorithms that are both methodologically innovative and highly impactful in the fight against climate change.