Develops theories of intelligence grounded in neural network internal structures, focusing on belief geometries in LLMs and biological brains. Conducts experiments bridging mathematics, ML interpretability, and safety research; requires PhD-level quantitative depth and hands-on coding.
140k – 200k
On-siteAI Research
About the role
Responsibilities
Develop unsupervised methods to recover belief geometries in real LLMs.
Extend theory of intelligence to complex cognitive tasks, RL, and biological brains.
Investigate generalization and out-of-distribution behavior in neural networks.
Participate in red teaming to stress-test the framework and identify edge cases.
Apply mathematics to biological neural networks using real brain data.
Requirements
Depth in at least one quantitative field (physics, mathematics, neuroscience, machine learning, etc.).
PhD or equivalent preferred.
Rigorous mathematics skills combined with hands-on work with models and data.
Ability to move between theory and experiment.
Cross-disciplinary learning (dynamical systems, probability, deep learning, physics, information theory, neuroscience).
Self-directed with ownership over research ideas.
Strong communication for collaboration.
Proficiency building code, experiments, and using AI tools.
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