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Develop and productionize zero-knowledge verification systems for frontier AI inference. Responsibilities include reducing proving costs, building proof circuits, emulating GPU floating-point behavior, designing sampling schemes, red-teaming constraint systems, validating soundness, publishing research, and collaborating with cryptographers, hardware-security specialists, policymakers, and systems engineers.

Design and evaluate software-only protocols for verifying AI datacenter compute and memory usage. The role involves developing challenge kernels, microbenchmarking GPUs, measuring latency across cluster topologies, studying compression and hidden capacity, red-teaming timing assumptions, and publishing findings. Work spans GPU performance engineering, adversarial measurement, cryptographic protocol design, hardware security, and LLM inference systems.

Own and scale the operations function for a rapidly growing international AI safety organization. Responsibilities include upgrading hiring, payroll, onboarding, expenses, office and applicant-management systems; establishing performance, talent, project-management and benefits processes; managing UK and Singapore entity infrastructure, compliance, offices and security; and partnering with leadership on culture, strategy, governance and policies. The role requires proactive execution, people leadership, stakeholder management, regulatory judgment and organizational scaling experience.

Expression of interest for future research scientist or research engineer opportunities at Neo Research. The organization seeks candidates working in model evaluations, AI alignment, interpretability, machine learning, large language model engineering, agentic systems, or related fields applicable to frontier AI safety. Prior AI safety experience is not required. Reading and writing Mandarin is a plus.

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.

Lead the AI safety research agenda, define and conduct rigorous studies of frontier-model risks, guide researchers, mentor teams, collaborate with engineers and external organizations, publish findings, recruit researchers, and shape the organization’s research culture and priorities.

Research Engineer/Scientist developing and testing confidential-computing verification systems for AI datacenters. Responsibilities include analyzing GPU attestation guarantees, designing attested-result registries and verification clients, building privacy-preserving logging and evaluation pipelines, chaining attestations, integrating multiple accelerator platforms, collaborating with cryptographers and hardware-security experts, red-teaming systems, and publishing results.