Senior Embedded Software Engineer

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, 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.

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