Senior Embedded Software Engineer (C/C++), Machine Learning

1 Day ago • 1 Years + • Research Development

Job Summary

Job Description

As a member of the Low Power AI solution team, you will play a critical role in deploying AI models on Qualcomm's low power AI accelerator. This involves mapping high-level machine learning operators to low-level hardware instructions, applying various optimization techniques like graph transformation, scheduling, and quantization. Your expertise will enhance inference efficiency and accuracy of models on Qualcomm's hardware architecture. You will also apply ML knowledge to extend frameworks, develop optimized software, and prototype complex algorithms, contributing to world-changing innovations.
Must have:
  • Deploy AI models on Qualcomm's low power AI accelerator.
  • Map high-level machine learning operators to low-level hardware instructions.
  • Apply optimization techniques: graph transformation, scheduling, memory planning, operator implementation, quantization.
  • Enhance inference efficiency and accuracy of models on Qualcomm's hardware.
  • Solid hands-on skills and experience on performance optimization.
  • Proficient programming skills in C/C++.
  • Experience with Linux/Android development environment and tools.
  • Familiar with embedded/computer hardware architecture.
  • Apply Machine Learning knowledge to extend training or runtime frameworks.
  • Model, architect, and develop machine learning hardware for inference or training solutions.
  • Develop optimized software to enable AI models deployed on hardware.
  • Collaborate with team members for joint design and development.
  • Assist with development and application of machine learning techniques into products/solutions.
  • Develop, adapt, or prototype complex machine learning algorithms, models, or frameworks.
  • Conduct complex experiments to train and evaluate machine learning models/software.
Good to have:
  • Master's degree in Computer Science, Engineering, Information Systems, or related field.
  • 2+ years of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras).
  • 2+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media).
  • 2+ years of experience with C/C++, ideally at the embedded level.
  • 2+ years of experience using statistics and probability (e.g., conditional probability, Bayes rule).
  • 2+ years experience working in a large matrixed organization.
  • 1+ year of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware.
  • 1+ year of work experience in a role requiring interaction with senior leadership (e.g., Director and above).
Perks:
  • World-class health benefit option providing world-class coverage to employees and their eligible dependents.
  • Programs designed to help employees build and prepare for a financially secure future.
  • Self and family resources to build emotional/mental strength and resilience, and define purpose.
  • Wellbeing programs and resources to help employees Live+Well and Work+Well.

Job Details

Job Description

Job Posting Date

2025-08-18

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

As a member of Low Power AI solution team, you will play a critical role at deploying AI models on Qualcomm's low power AI accelerator. The position focuses on mapping high level machine learning operators to low level hardware instructions, involving various optimization techniques: graph transformation, scheduling, memory planning, individual operator implementation, quantization, etc. Your expertise at machine learning is expected to enhance inference efficiency and accuracy of different models on Qualcomm's hardware architecture.

Skills / Experience Required:

  • Solid hands-on skills and experience on performance optimization.
  • Proficient programming skills in C/C++
  • Machine learning knowledge is a plus..
  • Experience with Linux/Android development environment and tools.
  • Familiar with embedded/computer hardware architecture.

Minimum Qualifications:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

OR

Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

OR

PhD in Computer Science, Engineering, Information Systems, or related field.

Preferred Qualifications:

  • Master's degree in Computer Science, Engineering, Information Systems, or related field.
  • 2+ years of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras).
  • 2+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media).
  • 2+ years of experience with C/C++, ideally at the embedded level
  • 2+ years of experience using statistics and probability (e.g., conditional probability, Bayes rule).
  • 2+ years experience working in a large matrixed organization.
  • 1+ year of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware.
  • 1+ year of work experience in a role requiring interaction with senior leadership (e.g., Director and above).

Principal Duties and Responsibilities:

  • Applies Machine Learning knowledge to extend training or runtime frameworks or model efficiency software tools with new features and optimizations.
  • Models, architects, and develops machine learning hardware (co-designed with machine learning software) for inference or training solutions.
  • Develops optimized software to enable AI models deployed on hardware (e.g., machine learning kernels, compiler tools, or model efficiency tools, etc.) to allow specific hardware features; collaborates with team members for joint design and development.
  • Assists with the development and application of machine learning techniques into products and/or AI solutions to enable customers to do the same.
  • Develops, adapts, or prototypes complex machine learning algorithms, models, or frameworks aligned with and motivated by product proposals or roadmaps with minimal guidance from more experienced engineers.
  • Conducts complex experiments to train and evaluate machine learning models and/or software independently.

Applicants

Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

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If you would like more information about this role, please contact Qualcomm Careers.

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Our employees make Qualcomm’s success possible. We hire the brightest minds and foster a supportive, inclusive culture where your ideas have the power to contribute to world-changing innovations and breakthrough technologies. To make that possible, we leverage the breadth and depth of our diverse expertise from around the world to answer the unasked, conquer the complex, and solve some of the biggest challenges only we can – together.

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