Research Engineer, Interpretability

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San Francisco, CA Posted 30+ days ago

The Interpretability team at the company 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…

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