Roles
All applications are rolling with no set deadline unless otherwise indicated.
In-person (preferred) or hybrid employee role at $181k–$240k per year.
In-person (preferred) or hybrid employee role at $158k–$209k per year.
In-person (preferred), hybrid, or remote employee role at $158k–$276k per year.
In-person (preferred) or hybrid employee role at $276k–$420k per year.
Trying our bounty may help you test whether you’d be interested in some of our work, and if you perform especially well we may consider you for a full-time role.
We’d like to hear from anyone who would like to work with us in any capacity!
About us
METR is a non-profit doing empirical research to test for whether frontier AI models possess the capability to permanently disempower humanity. We develop scientific methods to assess these risks accurately, and work with frontier AI companies (e.g., OpenAI, Anthropic), and government agencies to deploy these assessments. Our work helps ensure the safe development and deployment of transformative AI systems.
Some highlights of our work so far:
- Establishing autonomous replication evaluations: Thanks to our work, it’s now an industry norm to test models for autonomous capabilities (such as self-improvement and self-replication).
- Pre-release evaluations: We’ve worked with OpenAI and Anthropic to evaluate their models pre-release, and our research has been widely cited by policymakers, AI labs, and within government.
- Early commitments from labs: The safety frameworks of Google DeepMind, OpenAI, and Anthropic all credit or endorse our work in developing responsible scaling policies.
- Our work has been internationally recognized, e.g. by the UK government, President Obama, and Time Magazine.
- Inspiring lab evaluation efforts: Multiple leading AI companies are building their own internal evaluation teams, inspired by our work.
We are a motivated, fast-paced, growing team (currently ~20 people). Candidates should be excited about working entrepreneurially in a rapidly changing environment while helping to strengthen the organization's operational rigor.