Senior Embedded Software Engineer, Machine Learning runtime

Company:  Qualcomm Canada ULC

Job Area:  Engineering Group, Engineering Group  Machine Learning Engineering

General Summary
As a member of the Low Power AI Solutions team, you will play a critical role in enabling efficient deployment of AI models on Qualcomm's low-power AI accelerators. This position focuses on developing and optimizing the machine learning runtime framework for inference workloads on embedded edge devices. You will be responsible for implementing performance-critical components of the machine learning runtime framework and applying advanced optimization techniques.

This role includes adding runtime support for popular ML architectures that are best suited for Qualcomm's low-power AI accelerators. Your work will directly impact the runtime efficiency, latency, and power consumption of AI applications running on Qualcomm hardware. New Headcount.

Key Responsibilities

Design and implement core components of the ML runtime framework for inference on embedded systems.

Collaborate with compiler, hardware, and model teams to co-design efficient execution paths for AI workloads.

Develop and maintain C/C++ code for runtime kernels and system-level integration.

Develop tools to assist with performance profiling and debugging of quantized model accuracy

Analyze and improve runtime behavior using profiling tools and hardware counters.

Support deployment of models from popular ML frameworks (e.g., Onnx, Tensor Flow, PyTorch) onto Qualcomm's inference stack.

Required Skills & Experience

Strong hands-on experience in  performance optimization  for embedded or low-power systems.

Proficient in  C/C++ programming , with a focus on system-level and runtime development.

Solid understanding of  embedded system design , including memory hierarchy and hardware-software interaction.

Experience with  Linux/Android development environments  and tool chains.

Familiarity with  computer architecture , especially for AI accelerators or DSPs.

Basic knowledge of  machine learning concepts  and model structures.

Preferred Qualifications

Master's degree in Computer Science, Engineering, or related field.

2+ years of experience with ML frameworks (e.g., Tensor Flow, PyTorch, ONNX).

2+ years of experience in embedded system development and optimization for ML inference.

2+ years of experience with C/C++ in performance-critical environments.

Experience with low-level OS interactions (Linux, Android, QNX).

Familiarity with quantization, graph optimization, and model deployment pipelines.

Experience working in cross-functional teams and large matrixed organizations.

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.

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.

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

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 disabili 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.

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Pay range and Other Compensation & Benefits
$ - $

The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation  also…