GPU Chief Architect

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.

• 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 floor planning 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