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

Qualcomm•Markham, ON

6d•CA$114,400 - CA$164,400

About The Position

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. 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 in machine learning is expected to enhance inference efficiency and accuracy of different models on Qualcomm's hardware architecture.

Requirements

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.

Nice To Haves

Machine learning knowledge is a plus.
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).

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.