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SA
Singapore AI Safety Hub
Verification Lead
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What they do
A Verification Engineer is responsible for ensuring that a product meets its specifications and requirements. Designs methods to test products, identify bugs in software products, and communicates with cross-functional teams, ensuring product development procedures and outcomes meets regulations. Primary objective is to verify that the system functions correctly and reliably according to its design and intended purpose.
$158,679 / year median in the U.S.
Job Description
Verification Lead Singapore AI Safety Hub San Francisco, CA Job Details Full-time 5 hours ago Qualifications Project engineering Verification (System development task) Managing projects in an engineering role Coaching Team development Research project technical leadership Managing engineering teams Managing projects Full Job Description About the team The world is waking up to the fact that we will need ways to verify what's happening inside datacenters running large AI models — to enable international agreements, protect middle-power sovereignty, and facilitate trustworthy adoption of AI in high-stakes industries. But, for this to be trusted, it can't just be developed in a few countries. Singapore AI Safety Hub is launching the first international collaboration aimed at changing that. Our Verification team builds prototypes of these tools in public to speed up the development and adoption of globally trusted verification mechanisms. We're building tools that will translate into policy change in the real world because our team is doing more than just building. Our team is demonstrating these tools to policymakers globally, helping roadmap the path to production-scale verification mechanisms, and broadening the base of independent experts who can evaluate these tools. Our partners include experts from the Future of Life Institute, University of Oxford, and more. Our core team has experience at Oxford, ByteDance, Centre for the Governance of AI, and Singapore Government. Our collaborators have worked with Arm andIntel. Just this summer, we've presented our work at the AI Security Forum (Washington D.C.), ICML (Seoul), Australia AI Safety Forum (Sydney), and World AI Conference (Shanghai). Read more about the response here. Why Join SASH AI verification is still a young and highly talent-constrained field, while the need for credible mechanisms to support international coordination around advanced AI is becoming increasingly prominent, including in initiatives like Pacing the Frontier. Joining SASH now means having significant influence over which technical approaches get developed, what the team builds, and how this emerging field connects to real-world AI governance. Scenarios like AI 2040 illustrate the role technical verification mechanisms could eventually play in making international AI agreements credible in practice. SASH is a young, fast-moving organization where finding opportunities and making things happen is the norm. You'll have substantial autonomy to build around promising ideas rather than inherit a mature roadmap, while working with an international network of technical experts and policymakers. Your Work This is a critical leadership role with substantial scope to define the technical direction of SASH's Verification program. You'll help decide which technical bets are most important to pursue, turn promising ideas into practical prototypes, and build the team and capabilities needed to advance them. AI verification is still an open technical problem. Mechanisms need to be robust to attempts to evade them, privacy-preserving enough to be deployable, and auditable enough to earn the trust of governments and other stakeholders. Solving these problems can draw on cybersecurity, cryptography, ML, hardware, and electrical engineering — alongside an understanding of the real-world constraints under which verification mechanisms would operate. Our current project involves distinguishing between inference and training workloads on a GPU. Future directions could include scaling zero-knowledge proofs for AI inference or designing privacy-preserving approaches to white-box evaluations. In this role, you would: Set technical direction: Identify promising verification problems and approaches, decide which bets are worth pursuing, and steer projects from idea through prototype.