Anthropic STEM Fellow

Anthropic•San Francisco, CA

12d

About The Position

Anthropic is recruiting STEM researchers and experts for a four-month fellowship focused on applying Claude to challenging open problems in science and engineering. Fellows work in small pods with others in related fields, collaborating with Anthropic researchers to identify grand challenges, define meaningful progress, build datasets and simulations, and evaluate where Claude falls short of expert judgment. The work may involve using test-time compute strategies and integrating the scientific tools and software used in the Fellow’s field. Prior machine learning experience is helpful but not required; scientific judgment and a willingness to learn quickly are emphasized. The program runs full-time from January 12 to May 8, 2027. Fellows receive access to frontier Claude models and internal evaluation tooling, and attend weekly pod meetings with Anthropic researchers. A one-week in-person orientation in San Francisco is required. San Francisco is the default location, although remote engagements within the United States may be considered in certain circumstances. The position is a fixed-term contract, with Fellows engaged through an employer of record rather than employed directly by Anthropic. Some fellowships may be considered for extension. Compensation is $100,000–$167,000 for the four-month fellowship, equivalent to $300,000–$500,000 annualized, plus other benefits.

Requirements

Hold a PhD in a STEM field and have several years of experience, such as postdoctoral or faculty experience.
Have hands-on experience using Claude and Claude Code in a day-to-day workflow.
Be excited about AI’s potential to reason about difficult questions in the candidate’s field where expert judgment matters.
Be comfortable working in a fast-paced, high-agency environment for a few months and delivering concrete results.
Have existing authorization to work in the United States for the duration of the program.
Attend a one-week in-person orientation in San Francisco.

Nice To Haves

Prior machine learning experience is helpful but not required.
Scientific judgment and a willingness to learn quickly are emphasized.

Responsibilities

Identify long-horizon open problems in the Fellow’s field where Claude falls short of expert judgment, and define what meaningful progress would look like.
Build comprehensive datasets, simulations, and rigorous, hard-to-game evaluations to test whether models can plan experiments, interpret data, and reason about mechanisms.
Systematically identify where models are confidently wrong.
Apply Claude to open problems in the Fellow’s domain, using test-time compute strategies and integrating the scientific tools and software used in the field.
Collaborate in a small pod of Fellows and meet weekly with Anthropic researchers to share methods, pressure-test ideas, and develop work.