Research Scientist, Interpretability
Description
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Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights
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Design and run robust experiments, both quickly in toy scenarios and at scale in large models
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Create and analyze new interpretability features and circuits to better understand how models work.
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Build infrastructure for running experiments and visualizing results
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Work with colleagues to communicate results internally and publicly
Qualifications
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Have a strong track record of scientific research (in any field), and have done some work on Interpretability
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Enjoy team science – working collaboratively to make big discoveries
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Are comfortable with messy experimental science. We're inventing the field as we work, and the first textbook is years away
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You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results
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You can clearly articulate and discuss the motivations behind your work, and teach us about what you've learned. You like writing up and communicating your results, even when they're null
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This role is based in San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.