Member of Technical Staff, Embedded Assessments

We are a nonprofit research organization that develops scientific methods to assess AI capabilities, risks, and mitigations, with a specific focus on threats related to AI R&D automation and misalignment.

We believe it is robustly good for policymakers and civil society to have a clear understanding of risks from AI systems, and we are extremely excited to build a team of ambitious, excellent people to tackle one of the most important challenges of our time.

Overview METR has started embedding researchers inside frontier labs to investigate incidents, stress-test labs’ internal agent monitoring systems, and assess loss-of-control risks from internal deployment. As agent capabilities increase, we expect this to be one of the most important sources of independent information the world has about catastrophic risks from advanced AI.

As the source of information becomes more important, we'll need many more talented researchers and engineers who can conduct embedded exercises. We expect these assessors to have deep access, and for their work to be a large part of METR's impact in the next year. We want to build on the momentum from previous exercises to further develop our risk assessments.

What the role looks like You'll be embedded in a frontier AI lab for up to several weeks at a time, likely alongside 1-4 other METR staff. Between exercises, you'll practice, develop the general methodology, talk to other researchers, build tooling to make future exercises go better, help us hire and scale, write up results, and plan/coordinate future exercises.

Due to the nature of the work, there may be periods of weeks at a time, where you'd need to either relocate temporarily to a location closer to the frontier AI lab (e.g. downtown San Fransisco instead of our offices in Berkeley), or otherwise commit to commuting there daily. METR would provide lodging/travel support as needed.

We are looking for embedded researchers and engineers across multiple current and potential future exercises: AI R&D acceleration assessment, compute allocation, monitorability red-teaming, incident investigations, and more. We expect this role to evolve significantly as we develop and prototype this new form of risk assessment.

Ideal candidate We are looking for candidates with at least two of the following skills, although the ideal candidate will have most or all of them:

• You’re good at prompting LLMs, are familiar with research relevant to the fields above (or can get up to speed quickly).
• You have some security experience.
• You can get spun up on large codebases quickly.
• You’re scrappy, creative, independent, and self-directed (because during the exercises you’ll only have a few other METR employees you can talk to). The work is novel and you’ll need to figure a lot of stuff out on the fly largely by yourself.
• You are excellent at loss-of-control threat modeling and breaking down safety cases.
• You’re good at verbal communication, writing, and stakeholder management (e.g., navigating complex relationships with frontier labs).
• You’re trustworthy and have a good reputation.

Our culture METR is a mission-driven organization. We believe our work can meaningfully shape humanity's future for the better, and we want to be the best people in the world doing this work. We have a tight-knit, collaborative research culture rooted in truth-seeking and integrity. We're fiercely committed to producing high-quality, trustworthy science. We're honest and transparent about our results, especially when they may go against the grain. We've earned trust as reliable partners who handle confidential information with care. We maintain a low-ego, drama-free environment focused on what matters.

Hybrid Requirements: Our technical team members are in our office in Berkeley 3-5 days/week. Please let us know in your application if this is a constraint. If you lack US work authorization and would like to work in-person (strongly preferred), we can likely sponsor a cap-exempt H-1B visa for this role.

We encourage you to apply even if your background may not seem like the perfect fit! We would rather review a larger pool of applications than risk missing out on a promising candidate for the position.

Read the job posting on the METR site

Source: the METR Lever job board. We last checked this job 12 h ago. We first saw it 8 days ago. A job on a job board does not always mean that the team interviews people now.