Anthropic Fellows Program, AI Safety & Security

Overview

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.

What you'll do

• Some of the workstreams may include unique assessment steps; we therefore ask you for workstream preferences in the application .
• You can see an overview of the current workstreams below: AI Safety Fellows AI Security Fellows ML Systems & Performance Fellows Reinforcement Learning Fellows Economics & Societal Impacts Fellows This page is specific to one of the Anthropic Fellows Workstreams, see also the main Anthropic Fellows posting .
• Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.
• Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.
• Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures.
• AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations.

Requirements

• Some of the workstreams may include unique assessment steps; we therefore ask you for workstream preferences in the application .
• You can see an overview of the current workstreams below: AI Safety Fellows AI Security Fellows ML Systems & Performance Fellows Reinforcement Learning Fellows Economics & Societal Impacts Fellows This page is specific to one of the Anthropic Fellows Workstreams, see also the main Anthropic Fellows posting .
• Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.
• Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.
• Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures.
• AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations.

Skills