• Collaborate closely with researchers across statistics, cognitive science, and machine learning to design systems in which experimentation, inference, and uncertainty are first-class components of the learning process itself. • Designing and implementing adaptive experimental systems that operate continuously under nonstationarity, interference, and delayed or indirect outcomes. • Developing causal estimands, randomization schemes, and inference procedures whose primary goal is identifiability and validity, not just reward optimization. • Embedding rigorous experimental control directly into learning systems, including experimentation on the system’s own learning mechanisms, parameters, and representational choices. • Translating principles from experimental design, causal inference, and sequential decision-making into robust, always-on system behavior. • Implementing and maintaining research code that supports hierarchical experimentation, baseline control streams, and statistically valid online inference. • Creating diagnostics, monitoring tools, and guardrails to ensure learning systems remain calibrated and do not stabilize spurious structure over time. • Collaborating with interdisciplinary researchers to stress-test experimental learning mechanisms under realistic, adversarial conditions.