Sr Engineer: LPAI Architecture and Systems Engineer

Qualcomm · Hyderabad · 2+ yrs experience · Posted 2026-01-31

Tech stack: C, C++, Python

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About the role

Qualcomm India Private Limited
Engineering Group, Engineering Group > Systems Engineering
Responsibilities: - 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 value-tier platforms.
- Your focus will be on architectural analysis, optimization, and deployment of low-power AI (LPAI) solutions, enabling efficient on-device intelligence, including eNPU scheduling, memory hierarchy, compression/quantization strategies, and clock/BW voting, to enable efficient on-device AI for audio, 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
- Analyze, design, and optimize LPAI (Hexagon DSP, eNPU, TCM/UBUF/LLC/DDR) for performance, power, and area efficiency in value tier chipsets.
- Conduct architectural analysis and benchmarking of LPAI subsystems, identifying bottlenecks and proposing solutions for improved throughput and efficiency.
- Collaborate with hardware and software teams to define and implement enhancements in DSP/eNPU 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 LPAI, including quantization, compression, and hardware acceleration techniques.
- Support system-level integration, performance testing, and demo prototyping for commercialization of optimized DSP/eNPU solutions.
- Work closely with cross-functional teams to ensure successful deployment and commercialization of value tier chipset features.
- Document architectural analysis, optimization strategies, and performance results for internal and external stakeholders.
Qualifications: - Solid background in DSP architecture, embedded NPU design, and low-power AI systems.
- Proven experience in performance analysis, benchmarking, and optimization of embedded processors (DSP, NPU, ARM, RISC-V).
- Strong fundamentals of Power modeling and Power analysis
- Experience working on System level power anlaysis and measurements
- Strong programming skills in Embedded C/C++, Python familiarity with performance modeling tools.
- 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.
- Master’s or PhD degree in Engineering, Electronics and Communication, Electrical, Computer Science, or related field.
- Familiarity with Qualcomm DSP/eNPU architectures, SDKs, and tools.
- Experience with LPAI frameworks and design.
- Knowledge of audio or Sensor signal processing frameworks is a plus.
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.
- PhD in Engineering, Information Systems, Computer Science, or related field.

Qualifications

- Solid background in DSP architecture, embedded NPU design, and low-power AI systems.
- Proven experience in performance analysis, benchmarking, and optimization of embedded processors (DSP, NPU, ARM, RISC-V).
- Strong fundamentals of Power modeling and Power analysis
- Experience working on System level power anlaysis and measurements
- Strong programming skills in Embedded C/C++, Python familiarity with performance modeling tools.
- 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.
- Master’s or PhD degree in Engineering, Electronics and Communication, Electrical, Computer Science, or related field.
- Familiarity with Qualcomm DSP/eNPU architectures, SDKs, and tools.
- Experience with LPAI frameworks and design.
- Knowledge of audio or Sensor signal processing frameworks is a plus.
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.
- PhD in Engineering, Information Systems, Computer Science, or related field.

Responsibilities

- 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 value-tier platforms.
- Your focus will be on architectural analysis, optimization, and deployment of low-power AI (LPAI) solutions, enabling efficient on-device intelligence, including eNPU scheduling, memory hierarchy, compression/quantization strategies, and clock/BW voting, to enable efficient on-device AI for audio, 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
- Analyze, design, and optimize LPAI (Hexagon DSP, eNPU, TCM/UBUF/LLC/DDR) for performance, power, and area efficiency in value tier chipsets.
- Conduct architectural analysis and benchmarking of LPAI subsystems, identifying bottlenecks and proposing solutions for improved throughput and efficiency.
- Collaborate with hardware and software teams to define and implement enhancements in DSP/eNPU 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 LPAI, including quantization, compression, and hardware acceleration techniques.
- Support system-level integration, performance testing, and demo prototyping for commercialization of optimized DSP/eNPU solutions.
- Work closely with cross-functional teams to ensure successful deployment and commercialization of value tier chipset features.
- Document architectural analysis, optimization strategies, and performance results for internal and external stakeholders.

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