AI Experimental Systems Research Scientist – Causal Learning, Adaptive Experimentation

Job Description: • Collaborate 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. Requirements: • Ph.D. in Statistics, Biostatistics, Economics, Computer Science, Data Science, Operations Research, or a closely related field • Deep grounding in experimental design and statistical inference • Demonstrated ability to implement research-grade statistical or experimental methods in a general-purpose programming language (e.g., Python) • Experience working in research settings where the problem definition evolves and correctness takes precedence over convenience • Experience with adaptive or sequential experimentation (e.g., response-adaptive trials, causal bandits, best-arm identification) • Familiarity with causal inference frameworks spanning both design-based and model-based approaches • Strong intuition for identifiability, bias–variance tradeoffs, and statistical validity in complex, real-world settings • Experience working with nonstationary systems, concept drift, or delayed feedback loops • Experience reasoning about interference, carryover effects, time-varying treatments, or non-independent experimental units • Comfort designing experiments where the learning process itself is the object under experimental control • Familiarity with hierarchical or clustered experimental designs and multi-level inference • Interest in foundational questions about how autonomous systems should reason, experiment, and adapt in the world • Ability to communicate complex statistical ideas clearly to interdisciplinary collaborators • Curiosity, intellectual humility, and a strong preference for epistemic correctness over short-term performance gains. Benefits: • Medical, Dental & Vision • Health Savings Accounts • Health Care & Dependent Care Flexible Spending Accounts • Disability Benefits • Life Insurance • Voluntary Benefits • Paid Absences • Retirement Benefits Apply tot his job