AI Experimental Systems Research Scientist – Causal Learning, Adaptive Experimentation

🌍 Remote, USA 🚀 Full-time 🕐 Posted Recently

Job Description

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.

Ph.D. in Statistics, Biostatistics, Economics, Computer Science, Data Science, Operations Research, or a closely related field (completed and verified prior to start).
Deep grounding in experimental design and statistical inference, including randomized experiments and causal estimands.
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.