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GPU Software Development Engineer

GPU Software Development Engineer

CompanyIntel
LocationFolsom, CA, USA, Phoenix, AZ, USA
Salary$146520 – $206860
TypeFull-Time
DegreesBachelor’s, Master’s, PhD
Experience LevelMid Level, Senior

Requirements

  • Bachelor’s degree in Computer Science, Computer Engineering with 4+ years of relevant experience
  • OR Master’s degree in Computer Science, Computer Engineering, or Electrical Engineering with 2+ years of relevant experience, or should be pursuing a PhD of Science degree with 1+ years of work or academic experience
  • Experience with computer graphics hardware and software and GPUs
  • Software development is critical. C, C++. Python programming expertise and experience
  • Validation, Debugging/Triage experience at the ingredient or platform level, one of more of the following domains – Graphics, Media, Display Technology

Responsibilities

  • Graphics Driver/Application related validation and debug activities
  • Integrate upcoming graphics features into end-to-end validation flows
  • Triage and Debug reported failures and drive issue resolution with software and hardware development teams
  • Scale across end-to-end Display, Media, 3D, Compute and power conservation components
  • Analyze cross component features to develop robust end user test scenarios
  • Develop debug tools to improve graphics validation and debug efficiency
  • Enable new features for Content Creation applications, Gaming and AI domains to improve functionality and performance on graphics products

Preferred Qualifications

  • Experience in the following: Industry standard API’s and frameworks such as DirectX, OpenGL, OpenCL, Vulkan
  • Experience on latest Windows OS architecture
  • Framework and kernel debugging, Windows kernel internals
  • Debugging Windows Driver Model (WDM/WDF), Kernel Mode Driver
  • Understanding of media codecs and use-cases
  • Knowledge of device and system-level architecture, especially x86 based devices
  • Familiarity with various debug tools including Windbg, PIX, GPA, GPUView, emulators/JTAG-debuggers etc.
  • Understanding in state-of-the-art machine learning and deep learning algorithms, techniques and best practices, Expertise in Deep Learning Frameworks: TensorFlow, PyTorch