Job description

• Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights
• Design and run robust experiments, both quickly in toy scenarios and at scale in large models
• Create and analyze new interpretability features and circuits to better understand how models work.
• Build infrastructure for running experiments and visualizing results
• Work with colleagues to communicate results internally and publicly

Qualifications

• Have a strong track record of scientific research (in any field), and have done some work on Interpretability
• Enjoy team science – working collaboratively to make big discoveries
• Are comfortable with messy experimental science. We're inventing the field as we work, and the first textbook is years away
• You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results
• 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
• 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.