Our Client Currently looking for Senior Staff Engineer – GPU Architect

Role and Responsibilities

• Define and develop best?in?class GPU architecture and performance/power models for next?generation SoCs.
• Build and maintain cycle?accurate / performance / functional models of GPU subsystems (e.g., shader cores, fixed?function units, memory hierarchy, interconnect).
• Use modeling and profiling to explore architectural trade?offs (performance, power, area) and guide micro?architecture decisions.
• Analyze workloads (games, graphics benchmarks, GPU compute, AI/ML kernels) using simulation and hardware profiling to identify bottlenecks and optimization opportunities.
• Collaborate closely with model, RTL, DV, driver and compiler and performance teams to ensure architectural intent is correctly implemented, verified and tuned.
• Provide architectural input to compiler/driver/runtime teams to maximize utilization of GPU hardware through software optimizations.
• Develop methodologies, tools and automation flows for GPU performance estimation, capacity planning and regression analysis.
• Lead the debug and root?cause analysis of performance, power and bandwidth issues observed in models, emulation and silicon.
• Drive cross?team technical discussions, present modeling results and architectural proposals, and influence roadmap decisions for next?generation GPUs.

Main Requirements and Qualifications

• Bachelor, Master’s or higher in Computer Science, Electrical/Computer Engineering or a closely related field.
• Experience in GPU, graphics, high performance compute, AI accelerator, or related architecture/modeling areas.
• Strong experience with performance and/or cycle accurate modeling, 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, Metal).
• Proficiency in at least one modeling or implementation language (e.g., C/C++, SystemC, Python) and familiarity with scripting for data analysis and automation.
• Experience with performance analysis tools, profiling methodologies and workload characterization for games, benchmarks and/or GPU compute workloads.
• Good written and oral communication skills, with the ability to present complex technical topics clearly and drive consensus across teams.

Preferred Qualifications (Nice to Have)

• Experience with mobile/low power GPU design and power/performance trade off analysis.
• Background in compiler, driver, or runtime optimization 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.

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