Lead, Frontier AI Systems, Centre for AI Excellence

Responsibilities

• Define and evolve the strategic direction for the Frontier AI Systems & Capabilities workstream, setting priorities in response to rapid technical developments and ecosystem needs.
• Lead capability horizon scanning across frontier models and system architectures and analyse how emerging capabilities translate into system behaviours, deployment patterns, and operational constraints.
• Develop system foresight analyses that identify emergent behaviours, scaling dynamics, institutional gaps, and risk inflection points before large-scale deployment.
• Analyse model-to-system translation, including how advances in planning, memory, simulation, and coordination reshape autonomy, interaction, and oversight needs.
• Identify emergent failure modes at the model and system level, including breakdowns in coordination across agents, tools, organizations, and physical and digital environments.
• Develop capability- and system-centric frameworks for evaluation, assurance, and deployment, treating identity, permissions, observability, traceability, coordination, and interoperability as architectural design requirements.
• Lead analysis of embodied and spatially grounded AI systems to understand real-world interaction dynamics, irreversible failure modes, and control and assurance requirements for deployment in physical environments.
• Convene frontier labs, infrastructure providers, standards bodies, safety institutes, and policymakers to establish shared technical reference points that shape research, system design, and safeguards.
• Oversee workstream delivery, including planning, resourcing, stakeholder engagement, and reporting, ensuring high-quality outputs on tight timelines.

Requirements and Experience

• Advanced degree (Master’s) in AI, Systems Engineering, Computer Science, Business, or Technology Governance.
• 7+ years experience working on frontier AI capabilities, system architecture, AI safety, or advanced technology governance with a focus on real-world deployment.
• Demonstrated technical fluency in contemporary AI systems (e.g., foundation models, agentic systems, multi-agent coordination, evaluation methods, deployment infrastructure).
• Proven ability to analyse emergent system-level risk across models, agents, tools, and infrastructure.
• Familiarity with areas such as interoperability, identity, observability, and coordination protocols.
• Experience translating frontier technical research into actionable frameworks for C-level stakeholders, assurance mechanisms, standards, or policy instruments.
• Able to operate under high uncertainty and develop structured analysis in fast-moving environments.
• Strong track record engaging model developers, infrastructure providers, governments, or multilateral institutions, with executive-level communication and coalition-building capability.