This individual-contributor role combines AI, robotics, laboratory automation, and scientific reasoning to design, implement, and validate systems that perceive laboratory state, recommend actions, learn from experimental data, recover from errors, and improve experimental outcomes. Deep expertise in multiple relevant areas, such as world models, reinforcement learning, Bayesian optimization, causal inference, multimodal AI, robotics, or scientific machine learning.