Anthropic Research Resident

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.

About the role

The Anthropic Residency is our paid, fixed-term research program, with placements lasting between six weeks and six months. We are looking for people who have led ambitious work in a variety of technical fields and want to transition to full-time work focused on making sure AI systems are safe and beneficial for all of humanity. If you are a professor in a quantitative discipline, a technical founder or a staff-level engineer and want to transition to work on transformative AI, we want to hear from you. Background in machine learning is not required.

What to expect

A concrete, scoped project matched to your background, with direct mentorship from Anthropic researchers

Access to Claude models and GPU compute

Connection to the broader AI safety and security research community

Interview process

The interview process will include an initial application, recruiter screen, technical assessment, research discussion and a reference check.

Applications for this round close on November 22, 2026 (11:59 PM PT). We expect to make offers in January 2027.

You may be a good fit if you

Are motivated by making sure AI is safe and beneficial for society as a whole

Are excited to transition into working on empirical AI research full-time

Have led ambitious work in a variety of technical fields (professor in a quantitative discipline, a technical founder or a staff-level engineer)

Strong candidates may also

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

Have experience with LLMs, reinforcement learning or Kubernetes clusters and complex shared codebases

Potential Anthropic mentors include

Sara Price: leads the Alignment Training team; background in data science

Ethan Perez: leads the Alignment Science team; background in machine learning

Evan Hubinger: leads the Alignment Stress-Testing team; formerly at The Machine Intelligence Research Institute (MIRI)

Samuel Marks: leads the Scalable Oversight team; background in mathematics

Elana Simon: researches the interpretability of language models and biological models; background in biomedical informatics

Belinda Li: researches how language models represent the world, users, and themselves; background in natural language processing (PhD, MIT)

Jack Lindsey: leads the Model Psychology team; background in theoretical neuroscience

Nicholas Carlini: researches adversarial machine learning and AI security; previously at DeepMind and Google Brain

Jascha Sohl-Dickstein: works at the intersection of safety and pretraining; background in physics and neuroscience, formerly Principal Scientist at Google Brain

Residents will work on empirical research questions in collaboration with Anthropic's research teams. Areas we are particularly interested in include:

AI safety & alignment

Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.

Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.