The mission of the Capacity Engineering & Efficiency team is to provide input into our company-wide cloud infrastructure strategy and efficiency deliverables, with a specialized focus on ML Scheduling and Observability for our Compute infrastructure. You will develop and optimize scheduling systems for our large-scale machine learning workloads, particularly working with our Python-based scheduling architecture and orchestrating workloads across jobs. Your work will contribute to our path toward building RL-aware schedulers while supporting and improving our model development through improved observability and capacity efficiency. You will be expected to work with engineering teams to ensure optimal operation and growth of our infrastructure from both a cost and technology perspective, collaborate with research engineering to scope and understand the observability and capacity needs for model development, and partner cross-functionally with finance and data science teams to analyze and forecast growth.