Qualcomm
Senior Embedded Software Engineer (Cloud Edge & Data Center Machine Learning)
$118K–$177K
San Diego, California · On-site · Senior · Full-time · Posted Oct 1, 2026
- Experience
- 2+ years of low-level C programming; 2+ years of Software Engineering or related work experience with a bachelor's degree, or 1+ year with a master's degree, or a PhD. Also asks for 2+ years of academic or work experience with a programming language.
- Education
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field; alternatively, a Master's degree in one of these fields or a PhD in one of these fields.
About the job
Designs, develops, and commercializes embedded software and firmware for machine-learning use cases on Qualcomm cloud-edge and data-center silicon.
What you'll do
- Develop software and firmware involving low-level embedded systems, including BSPs, device drivers, and RTOS.
- Contribute to technologies including ML acceleration, memory management, virtualization, and efficient data movement.
- Work with internal and cross-functional teams on software design, development, debugging, and testing.
What they ask for
- Low-level C programming and C development, including debugging, testing, and unit testing.
- BSP and device-driver development, board bring-up, RTOS, secure boot, and TrustZone architecture.
- Experience with development and build tools for Embedded Linux applications; source and configuration management tools such as git/Gerrit.
- Bachelor's degree in a related field and 2+ years of Software Engineering or related work experience; alternatively, a Master's degree and 1+ year, or a PhD. Also requires 2+ years of academic or work experience with a programming language.
Nice to have
- Familiarity with ML frameworks such as PyTorch, JAX, and Llama.cpp.
- Familiarity with interfaces including PCIe, LPDDR, USB, and UFS, or Qualcomm QuRT RTOS and Core SW Infra.
Pay
$117,800.00 - $176,600.00 (USD; base salary)
Tools
C, GNU/LLVM, JTAG/SWD, BSP, RTOS, Embedded Linux, git/Gerrit, PyTorch
AI focus
Develops embedded software and firmware for cloud-edge and data-center machine-learning use cases, contributing to ML acceleration and related silicon technologies.
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