Anthropic · San Francisco · Hybrid

Research Engineer / Scientist, Alignment Science

4/29/2025

Description

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.
 
Our blog provides an overview of topics that the Alignment Science team is either currently exploring or has previously explored. Our current topics of focus include...
  • 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.
  • 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

  • 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

Nice to have

  • 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
  • 100% of the skills needed to perform the job
  • Formal certifications or education credentials

Benefits

USD 350000-500000

Application

View listing at origin and apply!

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