Research Scientist, Interpretability

About the role:

When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?"

The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. We’re looking for researchers and engineers to join our efforts.

People mean many different things by "interpretability". We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do "biology" or "neuroscience" of neural networks using “microscopes” we build, or as treating neural networks as binary computer programs we're trying to "reverse engineer".

Responsibilities:

• 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

You may be a good fit if you:

• 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

To learn more about the skills we look for and how to prepare for this role, see our blog post – So You Want to Work in Mechanistic Interpretability?

Familiarity with Python is required for this role.