GPU Chief Architect - Cambridge

We are seeking a highly experienced GPU architect to lead the definition and execution of next-generation mobile GPU architecture, 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.

Key Responsibilities

• 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.
• The 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.

Required

• 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.