Research Engineer / Scientist, Alignment Science

Anthropic
San Francisco, CA, USA2025-04-14

About the job

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.

Responsibilities

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.

Write scripts and prompts to efficiently produce evaluation questions to test models’ reasoning abilities in safety-relevant contexts.

Contribute ideas, figures, and writing to research papers, blog posts, and talks.

Run experiments that feed into key AI safety efforts at Anthropic, like the design and implementation of our Responsible Scaling Policy.

Qualifications

Minimum

Have significant software, ML, or research engineering experience

Have some experience contributing to empirical AI research projects

Have some familiarity with technical AI safety research

Prefer fast-moving collaborative projects to extensive solo efforts

Pick up slack, even if it goes outside your job description

Care about the impacts of AI

Preferred

Have experience authoring research papers in machine learning, NLP, or AI safety

Have experience with LLMs

Have experience with reinforcement learning

Have experience with Kubernetes clusters and complex shared codebases