Own the allocation solver beyond the CP-SAT baseline. Role shape: Own and advance Kyber's allocation solver beyond the CP-SAT baseline: more constraints, faster and more stable re-solves, and objectives informed by learned models rather than hand-encoded rules. Environment: Python, CP-SAT and OR-Tools, MIP and metaheuristics, incremental re-optimisation, benchmarking on live station data, Postgres, AWS, close work with the machine learning role. Experience: Minimum three years in applied optimisation or operations research. Practical reality: direct customer contact, time in live operations starting on the airport ramp, coding credits, London-based, remote only by exception, UK work authorization preferred. What We Are Looking For: You own the solver: Model formulation and decomposition; Warm starts and incremental re-solve; Heuristics and metaheuristics where exact methods stop scaling; Objective and stability design; A benchmarking harness built on real operational data. You decide what stays exact, what becomes learned, and where the boundary sits. The measure is a plan an operations manager trusts enough to run their shift on, and that holds when the day goes wrong. At least three years in applied optimisation... London-based, remote only by exception. UK work authorization preferred.