Description
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.
These papers give a simple overview of the topics the Alignment Science team works on: Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, Studying Large Language Model Generalization with Influence Functions, Debating with More Persuasive LLMs Leads to More Truthful Answers, Language Models (Mostly) Know What They Know, Measuring Progress on Scalable Oversight for Large Language Models, Measuring Faithfulness in Chain-of-Thought Reasoning, Discovering Language Model Behaviors with Model-Written Evaluations.
Note: Currently, the team has a preference for candidates who are able to be based in the Bay Area. However, we remain open to any candidate who can travel 25% to 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.
You may be a good fit if you:
- 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
Strong candidates may also:
- 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
Candidates need not have:
- 100% of the skills needed to perform the job
- Formal certifications or education credentials