Secure the AI apps and agents you build and deploy with red teaming and runtime guardrails powered by real-world adversarial data. Backed by Norwest and CRV.
About the role
You ship a benchmark every two to three weeks ( example benchmark ). Each one measures a frontier risk that nobody has measured yet. Some go public. Some go only to the labs. You will not write every eval yourself. Each benchmark pairs you with an in-house researcher who owns that harm area, and you get a budget for freelancers you direct. You own the taxonomy, the harness, the quality bar and the release. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- PhD or Masters in computer science, machine learning or a related field, or equivalent depth from industry research
- 3+ years building and running safety or security evaluations for language models in production, at an AI lab, a model provider, or a safety and security research organisation
- 5+ relevant research publications in the field of AI safety and security including lead author on at least 2 of them
- Strong engineer. Evaluation harnesses, distributed inference, vLLM, reading a codebase and fixing it
- You can build a taxonomy, not only score against one
- You can direct a researcher and two freelancers without managing them formally
More about this role
You ship a benchmark every two to three weeks ( example benchmark ). Each one measures a frontier risk that nobody has measured yet. Some go public. Some go only to the labs.
Some of the benchmarks and papers are done in collaboration with the leading AI Labs and universities.
You will not write every eval yourself. Each benchmark pairs you with an in-house researcher who owns that harm area, and you get a budget for freelancers you direct. You own the taxonomy, the harness, the quality bar and the release.
The seat sits in the CTO office alongside the research lead who sets our public research agenda. Around 150 researchers here work on harms directly, and you can pull any of them onto a subject.
A benchmark every two to three weeks. Size follows the subject. A chat-based taxonomy can carry 100 evals. An agentic or GRPO benchmark is closer to 20, because each one is expensive to read. Sensitivity decides what ships publicly and what goes to the labs alone.
The test is simple. A frontier lab reruns our set and gets our numbers. The verifiers hold, the rubrics are clear, the distribution is sane, and their subject matter experts read the taxonomy and call it novel.
That means you...
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