Research Scientist, Interpretability

Anthropic • San Francisco, CA

279d • $315,000 - $560,000

About The Position

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. 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, or as treating neural networks as binary computer programs we're trying to 'reverse engineer'. We aim to create a solid foundation for mechanistically understanding neural networks and making them safe. In the short term, we have focused on resolving the issue of 'superposition', which causes the computational units of the models, like neurons and attention heads, to be individually uninterpretable, and on finding ways to decompose models into more interpretable components. Our recent work finding millions of features on Sonnet, one of our production language models, represents progress in this direction. This is a stepping stone towards our overall goal of mechanistically understanding neural networks.

Requirements

• 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
• Familiarity with Python is required for this role

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
• Build infrastructure for running experiments and visualizing results
• Work with colleagues to communicate results internally and publicly

Benefits

• Competitive compensation
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues
• Optional equity donation matching

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Bachelor's degree