Research Scientist, Interpretability

Join to apply for the Research Scientist, Interpretability role at Anthropic About Anthropic

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.

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. Think of us as doing the "biology" or "neuroscience" of neural networks using microscopes we build, or treating neural networks as binary computer programs we are trying to reverse engineer.

For an overview of our work, see our research lead's introduction to Interpretability, the Hard Fork podcast episode, and our recent blog post with accompanying video. Some of our notable publications include "A Mathematical Framework for Transformer Circuits", "In-context Learning and Induction Heads", "Toy Models of Superposition", "Scaling Monosemanticity", and our Circuits’ Methods and Biology papers.

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.

Role Specific Location Policy

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.

Annual Salary

$315,000—$560,000 USD

Education Requirements

We require at least a Bachelor's degree in a related field or equivalent experience.

Location-based Hybrid Policy

Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa Sponsorship

We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification.

Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous…