Research Engineer / Research Scientist, Zero-Knowledge Verification

Remote: yes Posted: 2026-09-22

THE TEAM

Some of the most consequential decisions of the AI era will depend on answering one question: can we verify what is happening inside AI datacenters? Answering it could unlock international cooperation, help countries protect their sovereignty, and enable trustworthy adoption of AI in high-stakes industries. Building tools that can earn trust across borders is an urgent technical and political challenge.

SASH’s Verification team builds and tests tools to verify agreements about AI. We prototype new verification mechanisms, lead international research collaborations, and work with policymakers around the world to show what these tools can do and inform how they’re used.

You’d join a small team combining technical expertise with international AI policy experience. Our technical team is led by Pascal Berrang, an Associate Professor at University of Birmingham, and brings experience from the Singapore Government. Our policy team brings experience from Oxford and the Centre for the Governance of AI, while our partners include experts from the Future of Life Institute and the University of Oxford.

THE PROBLEM

An international agreement on AI is only as broad as what can be verified, and the appetite for verification is growing. In September 2026 Anthropic gave independent third-party evaluators permanent, employee-level access to its systems, and OpenAI said it would match it. Frontier developers now accept that outsiders should be able to check their work.

Between states, the options are cruder: take the operator's word that a datacenter is running a declared, safety-tested model and nothing else, or put inspectors and hardware inside the building. Inspections do not scale, and installing hardware might not be acceptable to every operator.

Zero-knowledge proofs are the one approach that removes both problems at once. The prover can demonstrate a claim — the committed model produced these output tokens — while revealing nothing about the weights, and the verifier's trust rests on mathematics rather than on hardware it has to manufacture, ship and guard. Progress here can be deployed immediately, because there is nothing to fabricate and nothing to install.

The catch has always been cost, and the figures usually quoted for it are badly out of date: the literature still puts proving at four orders of magnitude more expensive than the computation being proved. Our own prototype proves a full conversation with an 8-billion-parameter model in about an hour and a half on a single GPU, and most of what is left is ordinary engineering rather than cryptography. Extrapolated to a 405-billion-parameter model, fifty times larger, that is 16 to 24 hours because cost grows far more slowly than the models do.