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Qualcomm • San Diego, California, United States
Role & seniority: Senior GPU Engineer (requires 5+ years experience, or relevant advanced degree; PhD with 4+ years).
Stack/tools: GPU architecture/pipeline; SystemVerilog; Python; C/C++; RTL/SoC design; pre-silicon emulation, simulation, simulation-acceleration, data analytics (Python).
Perform pre-silicon performance analysis and closure on state-of-the-art GPU designs, identifying bottlenecks and proposing hw/sw optimizations for PPA.
Contribute to pre-silicon execution with extended benchmark coverage and virtual GPU bring-up using hardware-accelerated emulation to enable cross-team validation.
Analyze performance and architectural bottlenecks; collaborate with GPU Design/Architecture teams; execute HW-SW co-verification and big-data benchmark analysis.
Deep knowledge of GPU pipeline and architecture; SystemVerilog; Python; C/C++
5+ years’ experience in software/hardware/systems engineering or related field; RTL/SoC background or equivalent
Experience with RTL design, Simulation Acceleration, Emulation
Experience with SW/HW design partitioning and performance/f bottleneck analysis
Data analysis/visualization in Python; collaboration with System Level Architecture teams
Location & work type: Location not specified; full-time position.
Compensation note: pay range $195,200 – $292,800; plus bonus and RSU eligibility; benefits described by Qualcomm.
Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 6+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
OR
Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 5+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
Responsible for pre-silicon performance analysis and closure on state of art GPU design using trending graphic/compute/AI benchmarks. Work closely with design/arch/model teams on GPU pipeline bottleneck analysis and propose hw/sw optimizations to improve PPA.
Contribute to pre-silicon execution with additional benchmark functional coverage.
Virtual GPU bring-up using hardware accelerated emulation system to enable other sw/hw teams on pre-silicon functional execution.
Analysis of performance and architectural bottlenecks in the GPU designs
Work with GPU Design and Architecture Teams to understand concepts and performance bottlenecks in GPU.
Implement data analytics using Python.
HW-SW co-verification, simulation, simulation-acceleration, emulation, benchmark analysis, big-data analytics are all tools in our verification toolbox you will use on a daily basis.
Skills and experience we would love to see
GPU pipeline, System Verilog, Python, C/C++ skills required
Desirable Qualifications
Experience with GPU Architectures and pipeline analysis
RTL design experience and/or very strong SoC architecture background
Experience with Simulation Acceleration and Emulation
Experience working with System Level Architecture Teams to understand Performance Requirements and Bottlenecks
Experience with SW and HW design partitioning
Experience with analyzing performance issues in Software and Hardware
Data analysis using Python and Data Visualization
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