METR is developing evaluations for AI R&D capabilities, such that evaluators can determine if further AI development risks a “capabilities explosion”, which could be extraordinarily destabilizing if realized.
METR is hiring ML research engineers/scientists to drive these AI R&D evaluations forward.
Responsibilities:
- Produce tasks/benchmarks that can determine if a model is dangerous
- Run experiments to determine how elicitation techniques affect results on our evaluation suite
- Run evaluations internally, and potentially support external partners in governments and/or frontier AI companies in performing evaluations for autonomous capabilities
- Improve the tooling that researchers use for designing and running evaluations
- Collaborate closely with evaluation science researchers to develop a robust evaluation procedure
Your work will increase the odds that METR’s evaluation protocols can robustly predict whether a new frontier model poses catastrophic risks.
- Understand what kinds of abilities we need to be evaluating for, and what properties we most need our evaluations to have
- Find the most promising directions to explore; design experiments and research roadmap
- Collaborate with the threat modeling team, sharing your ML domain expertise to help us evaluate models for AI R&D skills
- Research execution
- Rapidly execute experiments, obtain reliable results
- Design sensible pipelines and workflows (know which things are going to be reused and need to be good versus what things it's ok to do scrappily)
- Quickly interpret results - recognize what is signal vs noise, notice when things don't look right and there might be a bug, know where to look for bugs in ML experiments
- Know how much work different approaches are likely to be and how promising they are; when you have uncertainties, get information as quickly as possible
What we’re looking for
- Have substantial ML research engineering experience, for example:
- Have research publications related to machine learning in which you played a major role,
- Have professional experience optimizing compute for inference or training of a large model, and/or
- Have played a significant role in the training or optimization of a large model
An ideal candidate would be a machine learning researcher with extensive experience working with frontier LLMs and a track record of successful execution-heavy research projects.
We expect to hire multiple people for this position, and their work will focus more on either the engineering or research side of the role, depending on their strengths.
About METR
METR is a non-profit which 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 AI companies 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.
Logistics
Deadline to apply: None. Applications will be reviewed on a rolling basis.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Apply for this job
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. If you lack US work authorization and would like to work in-person (preferred), we can likely sponsor a cap-exempt H-1B visa for this role.
We are committed to diversity and equal opportunity in all aspects of our hiring process. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We welcome and encourage all qualified candidates to apply for our open positions.
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