Sr Engineer: Embedded AI Architecture and Systems Engineer

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General Summary:

As part of Qualcomm’s Audio and Low-Power AI (LPAI) Architecture group, you will architect, analyze, and optimize DSP and embedded NPU (eNPU) performance across Snapdragon platforms. Your focus will be on architectural analysis, optimization, and deployment of machine learning software, enabling efficient on-device intelligence, including scheduling, memory hierarchy, compression/quantization strategies, to enable efficient on-device AI for audio, camera, sensors, and always-on use cases. You will build system models, conduct performance/power trade studies, and drive architectural recommendations that scale across mobile, XR, compute, IoT, and automotive tiers

Key Responsibilities

• Analyze, design, and optimize Machine learning kernels on ML HW accelerator for performance, power, and area efficiency in value tier chipsets.

• Conduct architectural analysis and benchmarking of ML subsystems, identifying bottlenecks and proposing solutions for improved throughput and efficiency.

• Collaborate with hardware and software teams to define and implement enhancements in ML HW microarchitecture, memory hierarchy, and dataflow.

• Develop and validate performance models for AI workloads, including signal processing and ML inference, on embedded platforms.

• Prototype and evaluate new architectural features for ML HW, including quantization, compression, and hardware acceleration techniques.

• Support system-level integration, performance testing, and demo prototyping for commercialization of optimized ML solutions.

• Document architectural analysis, optimization strategies, and performance results for internal and external stakeholders.

Requirements

• Solid background in DSP architecture, embedded NPU design, and low-power AI systems.

• Proven experience in performance analysis, benchmarking, and optimization on any embedded processors (DSP, ARM, RISC-V, NPU).

• Strong programming skills in Embedded C/C++, Python

• Experience with embedded platforms, real-time operating systems, and hardware/software co-design.

• Expertise in both fixed-point and floating-point implementation, with a focus on ML/AI workloads.

• Excellent communication, presentation, and teamwork skills; ability to work independently and across global teams.

• Strong fundamentals of Power modeling and Power analysis

Educational Qualifications

• Master’s or PhD degree in Engineering, Electronics and Communication, Electrical, Computer Science, or related field

Minimum Qualifications:

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.

OR

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

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