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Sr Engineer: LPAI Architecture and Systems Engineer at Qualcomm — Hyderabad

Experience2–7 yrs
SalaryNot disclosed
Work modeonsite
Posted23 Sept 2026

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About Qualcomm

Qualcomm is hiring in Hyderabad in semiconductors electronics. This role looks for around 2+ years of experience.

Skills

DSP architecture
embedded NPU design
low-power AI systems
performance analysis
benchmarking
optimization
Power modeling
power analysis
Embedded C/C++
Python
real-time operating systems
hardware/software co-design
fixed-point implementation
floating-point implementation

The role

An embedded software engineer at a semiconductor product company architects and optimizes low-power AI systems using DSP architecture and embedded NPU design for efficient on-device intelligence, while applying performance modeling and Embedded C/C++. The role also applies power analysis and Python to validate AI workloads across embedded platforms.

Full job description

Job 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 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

Key Responsibilities

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.

Requirements

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.

Educational Qualifications

Masters or PhD degree in Engineering, Electronics and Communication, Electrical, Computer Science, or related field.

Preferred Qualifications

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.

Minimum Qualifications

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.