Research Engineer Scientist, Alignment Science

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:

You want to build and run elegant and thorough machine learning experiments to help us understand and steer the behavior of powerful AI systems. You care about making AI helpful, honest, and harmless, and are interested in the ways that this could be challenging in the context of human-level capabilities. You could describe yourself as both a scientist and an engineer. As a Research Engineer on Alignment Science, you'll contribute to exploratory experimental research on AI safety, with a focus on risks from powerful future systems (like those we would designate as ASL-3 or ASL-4 under our Responsible Scaling Policy ), often in collaboration with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team.

• Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.
• AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.
• Alignment Stress-testing : Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.
• Automated Alignment Research: Building and aligning a system that can speed up & improve alignment research.
• Alignment Assessments : Understanding and documenting the highest-stakes and most concerning emerging properties of models through pre-deployment alignment and welfare assessments (see our Claude 4 System Card ) , misalignment-risk safety cases, and coordination with third-party evaluators.
• Safeguards Research : Developing robust defenses against adversarial attacks, comprehensive evaluation frameworks for model safety, and automated systems to detect and mitigate potential risks before deployment.
• Model Welfare: Investigating and addressing potential model welfare, moral status, and related questions. See our program announcement and welfare assessment in the Claude 4 system card for more.

Note: For this role, we conduct all interviews in Python and prefer candidates to be based in the Bay Area.

Representative projects:

• Testing the robustness of our safety techniques by training language models to subvert our safety techniques, and seeing how effective they are at subverting our interventions.
• Run multi-agent reinforcement learning experiments to test out techniques like AI Debate .
• Build tooling to efficiently evaluate the effectiveness of novel LLM-generated jailbreaks.