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.

Think of us as doing "neuroscience" of neural networks using "microscopes" we build - or reverse-engineering neural networks like binary programs.

More resources to learn about our work:

Our research blog - covering advances including Monosemantic Features and Circuits

An Introduction to Interpretability from our research lead, Chris Olah

The Urgency of Interpretability from CEO Dario Amodei

Engineering Challenges Scaling Interpretability - directly relevant to this role

60 Minutes segment - Around 8:07, see a demo of tooling our team built

New Yorker article - what it's like to work on one of AI's hardest open problems

Even if you haven’t worked on interpretability before, the infrastructure expertise is similar to what's needed across the lifecycle of a production language model:

Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips

Research Engineer, Interpretability

Responsibilities

Benefits

Company

Anthropic

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