Open Roles at METR

Open Roles at METR

Roles

All applications are rolling with no set deadline.

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Machine Learning Research Engineer/Scientist

Job Description, Application

This is an in-person (preferred) or remote full-time employee role.

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Senior Machine Learning Research Engineer/Scientist

Job Description, Application

This is an in-person (preferred) or hybrid full-time employee role.

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Eval Production Lead

Job Description, Application

This is an in-person (preferred) or hybrid full-time employee role.

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Task Bounty

Bounty Instructions

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.

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Expression of Interest

Information, Form

We’d like to hear from anyone who would like to work with us in any capacity!

About us

METR does empirical research to determine whether frontier AI models pose a significant threat to humanity. It’s robustly good for civilization to have a clear understanding of what types of danger AI systems pose, and know how high the risk is. You can learn more about our goals from our videos (overall goals, recent update).

Some highlights of our work so far:

  • Establishing autonomous replication evals: Thanks to our work, it’s now taken for granted that autonomous replication (the ability for a model to independently copy itself to different servers, obtain more GPUs, etc) should be tested for. For example, labs pledged to evaluate for this capability as part of the White House commitments.
  • 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.
  • Inspiring lab evaluation efforts: Multiple leading labs are building their own internal evaluation teams, inspired by our work.
  • Early commitments from labs: Anthropic credited us for their recent Responsible Scaling Policy (RSP), and OpenAI recently committed to releasing a Risk-Informed Development Policy (RDP). These fit under the category of “evals-based governance”, wherein AI labs can commit to things like, “If we hit capability threshold X, we won’t train a larger model until we’ve hit safety threshold Y”.

We’ve been mentioned by the UK government, Obama, and others. We’re sufficiently connected to relevant parties (labs, governments, and academia) that any good work we do or insights we uncover can quickly be leveraged.