Qualcomm

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.

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, TensorFlow, 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 toolchains.
• 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., TensorFlow, 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.

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.

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).

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