Research Scientist - Simplex
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.
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.
Preferred
- Extensive ML experience.
- Experience in interpretability.
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