Research Engineer / Scientist, Anthropic

• Have significant applied software, ML, or research engineering experience
• Have experience contributing to empirical AI research projects and/or technical AI safety research
• Can reliably turn abstract theories into creative, tractable research hypotheses and experiments
• Prefer to move fast and iterate rather than run long extensive projects
• Are excited to dive into new technical areas on a regular basis
• Care about the possible impacts of AI development on humans and the AI systems themselves
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience

Desirable

• 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
• As a Research Engineer/Scientist within the newly formed Model Welfare program, you will be among the first to work to better understand, evaluate, and address concerns about the potential welfare and moral status of AI systems
• You are curious about the intersection of machine learning, ethics, and safety and are adept at navigating technical and philosophical uncertainty
• You’ll run technical research projects to investigate model characteristics of plausible relevance to welfare, consciousness, or related properties and will design and implement low-cost interventions to mitigate the risk of welfare harms
• Your work will often involve collaboration with other teams, including Interpretability, Finetuning, Alignment Science, and Safeguards
• Our announcement of the model welfare program, and our welfare assessment of Claude Opus 4 (published in the System Card) give a sense of some of our early work
• Investigate and improve the reliability of introspective self-reports from models
• Collaborate with Interpretability to explore potentially welfare-relevant features and circuits
• Improve and expand our welfare assessments for future frontier models
• Evaluate the presence of potentially welfare-relevant capabilities and characteristics as a function of model scale
• Develop strategies for making high-trust/verifiable commitments to models
• Explore possible interventions and deploy them into production (e.g. allowing models to end harmful or distressing interactions)