Anthropic

Research scientist at Anthropic whose public work includes AI alignment, reinforcement learning from human feedback, and model behavior.

Senior research engineer at Anthropic interested in agent foundations, model organisms of misalignment, and human-computer interaction.

Member of technical staff at Anthropic and associate professor of computer science, data science, and linguistics at New York University on leave. His public homepage focuses on natural language processing, machine learning, and AI alignment.

Researcher at Anthropic whose public homepage and scholarly profile connect cognitive science research with AI.

Anthropic researcher on the Frontier Red Team focused on cyber misuse evaluation and threat modeling; previously a physics PhD student at UC Berkeley and now also mentors in the MATS program.

Research scientist at Anthropic working on machine learning systems and AI; previously worked on machine learning systems, compilers, and sustainability at Google.

Co-founder and head of policy at Anthropic. He previously served as policy director at OpenAI, worked as a technology journalist, and writes the Import AI newsletter.

Associate Professor at the University of Toronto whose research spans deep learning, probabilistic modeling, and machine learning methods for science and AI safety.

Researcher focused on AI safety, reinforcement learning, and language models, with public work spanning red teaming, adversarial robustness, and model behavior.

Staff software engineer at Anthropic building systems for AI safety, reliability, and alignment.

Anthropic researcher working on machine learning and AI-assisted science; previously built tools for learning from text, images, and tabular data.

Research scientist known for mechanistic interpretability and deep learning visualization, previously at Google Brain and OpenAI.

Nora Belrose is an AI researcher whose work studies neural language models, latent structure, and cognition. She has contributed to Anthropic research on tracing and interpreting reasoning in large language models.

Assistant Professor of Philosophy at The University of Hong Kong and Research Fellow at Anthropic, working in ethics, epistemology, and social and political philosophy.

Jesse Mu is a Research Scientist at Anthropic and a visiting researcher at Stanford University. His work spans machine learning, AI safety, reinforcement learning, and deep learning theory.

Research scientist at Anthropic focused on safety and robustness for language models and reinforcement learning.

Research scientist at Anthropic working on reasoning and geometry-aware machine learning.

Associate Professor of Computer Science at the University of Toronto and director of the machine learning group, with research spanning probabilistic models and optimization algorithms.

Alignment researcher at OpenAI working on making AI understandable to and aligned with human values.

Anthropic co-founder and Chief Science Officer. Formerly a physicist at Johns Hopkins, he helped develop scaling laws for neural language models and works on the science and safety of large AI systems.

Anthropic researcher whose work includes reinforcement learning from human feedback and Constitutional AI; previously a Sherman Fairchild Postdoctoral Scholar in theoretical high-energy physics at Caltech.

Independent researcher working on the theoretical foundations of AI, especially inductive biases, scaling laws, and approximate Bayesian updating. His public homepage notes prior research roles at Anthropic and OpenAI.

Research scientist at Anthropic and assistant professor of computer science at Northeastern University working on interpretability and model understanding.

Member of Anthropic's Interpretability team, where he works on understanding how large language models work.