Senior Silicon Engineer, GPU
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Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
- 5 years of experience in device driver development (e.g., Linux/Android Kernel or RTOS).
- Experience in GPU architectures (e.g., pipelines, memory hierarchies, and command submission).
- Experience with graphics or compute APIs (e.g., Vulkan, OpenGL ES, or OpenCL).
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- 8 years of experience with GPU Compiler interactions or Low Level Virtual Machines (LLVM)-based backends or GPU performance improvement.
- Experience with pre-silicon emulation environments (Palladium, Zebu, or Veloce) and developing and debugging native code on Android.
- Knowledge of Memory Management Units (IOMMU/GMMU), power management, and interrupt handling.
- Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) and how they leverage GPU kernels.
- Track record of shipping production-quality drivers for high-volume consumer electronics.
About the job
As a Senior Graphics Processing Unit (GPU) Silicon Engineer in the Google Silicon team, you will be at the forefront of defining how our custom-designed hardware interacts with the entire software stack. You will drive the architecture and implementation of kernel-mode and user-mode drivers that power everything from high-end mobile graphics. You aren't just writing code, you are bridging the gap between hardware intent and software execution.
Responsibilities
- Design, implement, and maintain GPU drivers (Kernel and User mode) for custom Google Silicon.
- Lead the 'first-light' activities for new GPU IP on pre-silicon (emulation/FPGA) and post-silicon platforms.
- Profile and optimize the driver stack to eliminate bottlenecks, reduce overhead, and improve power efficiency.
- Implement and support industry-standard Application Programming Interfaces (APIs) such as Vulkan, OpenCL, and potentially proprietary compute extensions for Machine Learning/Artificial Intelligence (ML/AI).
- Partner closely with GPU Architects, Register-Transfer Level (RTL) Designers, and Operating System (OS) teams (Android/Linux) to influence future hardware features and software interfaces.
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