GPU Chief Architect in Cambridge

Our client is one of the world's leading technology companies with over 200,000 employees and operating in 170 countries around the world. Their spirit of innovation has led our client to work in close partnership with leading academic institutions in the UK to develop and refine the latest technologies, having invested in dedicated R&D facilities and teams in Cambridge, London, Edinburgh, and Ipswich (which are part of a global R&D team of almost 100,000 people). We are looking for a GPU Chief Architect to ideally be based in the Cambridge office and will offer a world-class remuneration package, and our client can offer relocation assistance and visa sponsorship.

We are seeking a highly experienced GPU architect to lead the definition and execution of next-generation mobile GPU architecture in our Kirin SOC, while driving architectural convergence between GPU and NPU toward a coherent xPU sub-system design. This role requires deep expertise in GPU microarchitecture, strong system-level architectural capability, including both hardware and software, and a thorough understanding in graphics and AI common workload. A proven track record of delivering related sub-system IP or complex SoC silicon is highly desirable.

The successful candidate will lead the effort in shaping a converged xPU architecture native for future AI compute, optimised for performance, power efficiency, and silicon area in the next generation mobile compute platforms.

• xPU Converged Architecture Design: Based on 1st order principle, analyse and characterise future mobile graphics and AI workload, redefine an xPU (GPU & NPU) converged architecture, including hardware and software, from the ground up that is optimal for future applications. Ensure compatibility or easy transition from the old architecture. Define unified or partially unified execution resources (vector, scalar, tensor units). Develop shared scheduling and workload dispatch mechanisms for graphics and AI. Design resource sharing and isolation strategies under mixed workloads. Evaluate architectural trade-offs between dedicated and converged compute blocks.
• Mobile GPU Architecture Leadership: Ensure the timely delivery of next-generation mobile GPU architecture and long-term roadmap. Lead evolution of shader cores, execution pipelines, and cache hierarchy. Drive performance, power efficiency (Perf/W), and area efficiency (Perf/mm²). Provide architectural leadership from concept phase through tape-out.
• Memory & Interconnect Architecture: Define a memory hierarchy strategy for converged GPU/NPU workloads. Architect shared cache structures and bandwidth arbitration policies. Optimise on-chip interconnect for heterogeneous compute traffic. Reduce data movement overhead across compute domains.
• System-Level Architecture Collaboration: Collaborate with CPU, AI software, runtime, and system architecture teams. Participate in SoC-level power, thermal, and floorplanning trade-offs. Align hardware architecture with graphics APIs and AI frameworks. Support performance modelling, workload characterisation, and silicon bring-up.

• 15+ years of experience in GPU, AI accelerator, or heterogeneous compute architecture.
• Deep understanding of GPU microarchitecture (SIMD/SIMT, scheduling, memory systems).
• Strong knowledge of tensor/matrix computation and AI acceleration techniques.
• Proven experience delivering high-volume silicon.
• Expertise in performance modelling and power analysis.
• Strong cross-functional communication and leadership capability.