AI Experimental Systems Research Scientist – Causal Learning, Adaptive Experimentation
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Job Description
AI Experimental Systems Research Scientist collaborating on always-on learning systems at 3M’s Corporate R&D. Designing rigorous experimental systems that adapt in complex environments to ensure accuracy and validity.
Responsibilities:
- 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
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