Teach Kyber to behave like the best coordinator. Role shape: Own Kyber's data and modelling layer: infer the temporal dependencies of live operations, learn how coordinators allocate, and close the loop between real-time execution and forward scheduling. Environment: Python, Postgres, AWS, time-series and sequence models, learned cost functions, imitation and reinforcement learning, data pipelines, evaluation harnesses, technical hiring, on-site deployment, close work with the optimization role. Experience: Minimum three years applying machine learning in production. Practical reality: direct customer contact, time in live operations starting on the airport ramp, meaningful founding equity, coding credits, London-based, remote only by exception, UK work authorization preferred. What We Are Looking For: You own the data and the modelling: The data pipelines and operational event labelling; The feature layer; The work of making live data trustworthy before anything is built on it. You propose the architecture, covering: Task duration and delay propagation; Disruption forecasting; Learned cost functions that feed the scheduler; The reinforcement loop that lets forward scheduling compound over time. At least three years applying machine learning in production... London-based, remote only by exception. UK work authorization preferred.