Machine Learning Engineer

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. New Position

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ 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 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

OR

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

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.
• 5+ years of experience with ML frameworks (e.g., TensorFlow, PyTorch, ONNX).
• 5+ years of experience in embedded system development and optimization for ML inference.
• 5+ 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.

Pay range and Other Compensation & Benefits

$131,200.00 - $181,200.00

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 at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer.

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