Senior AI Software Engineer - GenAI & Edge AI Stack

MaC VC

San Diego (CA)

On-site

USD 141,000 - 211,000

Full time

5 days ago
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Job summary

Qualcomm Technologies, Inc. is seeking an AI Software Core Software Engineer to develop and implement machine learning techniques for the Qualcomm AI Stack (QAIRT) and Genie back-end on Snapdragon platforms.

You will collaborate across OS environments and teams to optimize performance and memory usage for embedded AI workloads. You will contribute to a suite of in-house tools, research new AI toolchains, and ensure robust, scalable software across Android, Linux, QNX, Windows, and Zephyr

Qualifications

  • Bachelor's degree in CS/Engineering/IS or related field with 2+ years related work experience.
  • Master's degree in CS/Engineering/IS or related field with 1+ year related work experience.
  • PhD in CS/Engineering/IS or related field.
  • 1+ years of embedded software experience is preferred.

Responsibilities

  • Develop in-house software tools and research third-party toolchains/compilers for Qualcomm AI Stack SDKs (QAIRT/Genie).
  • Support execution of latest generative AI models on Snapdragon platforms across OSes.
  • Perform system-level performance and memory profiling for Edge-AI use cases.
  • Debug complex issues, perform root-cause analysis, ensure high system reliability.
  • Collaborate with cross-functional teams to deliver robust toolchains.
  • Contribute to technical excellence and knowledge sharing in the AI Software team.
  • Participate in design and code reviews.
  • Work with minimal supervision.

Skills

C/C++/Python
LLVM/GNU toolchains
Make/CMake

Education

Bachelor's degree in CS/Engineering/IS
Master's degree in CS/Engineering/IS
PhD in CS/Engineering/IS

Tools

LLVM
GNU
Make
CMake
Git

Job description

Qualcomm Technologies, Inc. is seeking an AI Software Core Software Engineer to develop and implement machine learning techniques for the Qualcomm AI Stack (QAIRT) and Genie back-end on Snapdragon platforms.

You will collaborate across OS environments and teams to optimize performance and memory usage for embedded AI workloads. You will contribute to a suite of in-house tools, research new AI toolchains, and ensure robust, scalable software across Android, Linux, QNX, Windows, and Zephyr

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