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Singapore AI Safety Hub

Research Engineer - AI Verification

Posted Yesterday
In-Office or Remote
Hiring Remotely in Singapore
Entry level
In-Office or Remote
Hiring Remotely in Singapore
Entry level
Build practical prototypes for AI verification, including distinguishing GPU training from inference workloads and exploring zero-knowledge proofs and privacy-preserving evaluations. The role spans ML engineering, inference pipelines, algorithms, frontend demonstrations, cybersecurity, and collaboration with external experts. It also involves technical communications and engagement with policymakers and international partners in an ambiguous, early-stage environment.
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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 and Intel.

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 working on technical problems where the solution space is still open, with significant room to explore approaches, build prototypes, and influence what gets 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

As a Research Engineer, you'll help turn emerging ideas in AI verification into practical technical prototypes. We're seeing more interest in our prototypes than we currently have capacity to pursue, so additional engineering capacity will directly expand the mechanisms and approaches we're able to explore.

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 ML engineering, cybersecurity, cryptography, hardware, and systems engineering.

Our current project involves distinguishing between inference and training workloads on GPUs. Future projects could include scaling zero-knowledge proofs of AI inference and developing privacy-preserving approaches to white-box evaluations.

In this role, you would:

  • Build prototypes supporting AI verification.

  • Support technical communications.

  • Collaborate with external experts.

  • Work across: ML engineering, inference pipelines, algorithms, frontend demos, and cybersecurity.

About YouEssentials
  • Strong ML engineering fundamentals.

  • Familiarity with AI verification, cybersecurity and/or confidential computing.

  • Comfortable in ambiguous early-stage environments.

Nice to Have
  • AI hardware engineering.

  • Confidential computing.

  • Cybersecurity instrumentation.

  • Startup experience.

  • Interest or experience working with Singaporean or Chinese stakeholders.

Role Logistics & BenefitsLocation:
  • This is a remote role that can be based in Singapore, the UK, or remotely.

  • For candidates interested in relocating, SASH can provide visa sponsorship in the UK.

  • Our team is globally distributed, so remote team members should be comfortable maintaining some working-hour overlap with colleagues across regions.

Compensation:

Our compensation takes location, experience, and level into account. Salaries above the stated range may be available for exceptional candidates.

Benefits:
  • Competitive benefits and leave policies.

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