Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
Mercor Safety Research Grants $5M
One of the biggest challenges the industry faces today is addressing whether frontier AI is safe enough to deploy. A model can pass safety checks and still act differently in production. This is why investing today in safety research, evals and verification is critical.
Mercor is committing $5 million to fund safety research. Award size and mix depend on the project. The grant supports:
Researcher hours
API costs
Stipends for event and conference attendance
The time of experts from Mercor's platform
This is separate from the Mercor Research Fellowship, which funds benchmark and economics work. You can apply to that HERE.
What we're looking for
We're open to proposals across the full range of safety interests. We're particularly interested in:
Misalignment: deceptive alignment, goal misgeneralization, reward hacking, scheming, and situationally-aware failure modes
Sandbox escape: containment failures, privilege escalation, tool misuse, and agents operating outside their intended scope
Evaluation awareness: models detecting they are being tested and behaving differently under observation than in deployment
Interpretability: understanding what models are actually doing internally, and whether that can be made legible to a human reviewer
Oversight and control: scalable supervision, human-in-the-loop reliability, and what breaks when the system is more capable than its reviewer
Red-teaming methodology: more robust systems for uncovering novel failures
If your work doesn't fit neatly into these, apply anyway. Strong proposals outside this list are welcome.
Why us
Funding for researcher time, API costs, and event attendance
Where useful to the work: access to Mercor's expert network for human grading, red-teaming, and annotation: lawyers, accountants, engineers, scientists, clinicians
Access to Mercor's internal evaluation infrastructure, subject to review
Introductions to Mercor's network of researchers across frontier labs and academia
Who should apply
Researchers at universities and non-profit research organizations, as well as public benefit corporations and small startups that do not sell AI training data. Awards are made to the researcher’s institution.
People with a specific, well-scoped question: the grant is built around your proposal, not a generic research rotation
Applicants based in the United States or the United Kingdom
Background in ML, CS, statistics, or an adjacent field (measurement, psychometrics, HCI, security, social science)
Bonus: experience with agentic evaluation, RL environments, adversarial ML, or systems security
We expect grantees to publish. A paper, an open dataset, a public methodology, or a tool the field can use, released under an open license that permits commercial use.
Award terms
Selected grantees will sign Mercor’s research award agreement. Key terms include:
Grantees keep ownership of their work and grant Mercor a non-exclusive license to use it, including commercially.
Mercor may identify grantees and their institutions as award recipients, including by name and logo. Grantees may use Mercor’s logo with Mercor’s approval.
For benchmark and evaluation projects, Mercor receives early access to the benchmark before public release and the exclusive right to build and host a private held-out test set, at Mercor’s cost.
Grantees agree not to work with specified Mercor competitors on substantially similar benchmarks or evaluations during the award and for 12 months afterward.
Grantees are free to publish their findings. Mercor reviews drafts in advance only to remove its confidential information.
Awards are subject to tax documentation, sanctions screening, and periodic progress reports.
How to apply
Submit an Expression of Interest. We expect to see a one- or two-page document. It should contain at least a section on your team, background, and research accomplishments; a section on your proposed research project; and a section on the outputs and impact of the project, with directionally correct timelines and resource requirements. Please do not include confidential or proprietary information in your Expression of Interest. Submitting an Expression of Interest does not obligate Mercor to make an award.
