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Staff Engineer - GPU Architect
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Staff Engineer - GPU Architect | AI Accelerators & High-Performance Compute | Cambridge
A global fabless semiconductor giant that enables nearly 2 billion connected devices a year is looking for a Staff Engineer – GPU Architect to join their GFX IP team in Cambridge. This industry leader develops innovative systems-on-chip (SoC) for mobile, home entertainment, connectivity, and IoT products, holding the number one position in Wi-Fi supply worldwide. The GFX IP developed by this team is deployed in flagship mobile SoCs and serves as fundamental technology for adjacent markets like laptops, AI, VR/AR, and automotive.
In this role, you will define and develop best-in-class GPU architecture and performance/power models for next-generation SoCs. You will build cycle-accurate, performance, and functional models of GPU subsystems, exploring architectural trade-offs to guide micro-architecture decisions. The position involves analyzing workloads like games, graphics benchmarks, and AI/ML kernels using simulation and hardware profiling to identify bottlenecks. You will collaborate closely with RTL, DV, driver, compiler, and performance teams to ensure architectural intent is correctly implemented, verified, and tuned, while driving cross-team technical discussions to influence future roadmap decisions.
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Main Requirements
- Bachelor’s, Master’s, or higher in Computer Science, Electrical/Computer Engineering, or a closely related field.
- More than 7 years of work experience, with a background in GPU, graphics, high-performance compute, AI accelerator, or related architecture/modelling areas.
- Strong experience with performance and/or cycle-accurate modelling, simulation frameworks, or architectural exploration for complex SoCs or accelerators.
- Solid understanding of graphics and compute APIs (such as Vulkan, OpenGL, DirectX) and/or GPU compute frameworks (e.g., OpenCL, CUDA).
- Proficiency in at least one modelling or implementation language (e.g., C/C++, SystemC, Python) along with scripting for data analysis and automation.
- Experience with performance analysis tools, profiling methodologies, and workload characterisation.
- Good communication skills with the ability to present complex technical topics clearly and drive consensus across teams.
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Preferred Qualifications
- Experience with mobile/low-power GPU design and power/performance trade-off analysis.
- Background in compiler, driver, or runtime optimisation for GPUs or accelerators.
- Familiarity with ML/AI workloads, DNN operators, and their mapping onto GPU or accelerator architectures.
- Experience collaborating with silicon implementation, physical design, and DV teams on performance and power sign-off.
Interested? Apply directly through LinkedIn, or send your CV to george@eu-recruit.com
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