Sr Engineer: Embedded AI Architecture and Systems Engineer

QUALCOMM, Inc.; Hyderabad; On-site; INR 2,500,000 - 3,600,000; Full time

Job summary

Qualcomm India Private Limited seeks an experienced Systems Engineer to architect, analyze, and optimize DSP and embedded NPU performance across Snapdragon platforms. You will drive architectural analysis, performance/power tradeoffs, and on-device AI optimizations for audio, camera, sensors, and always-on use cases.

Collaborating with hardware and software teams, you will define ML HW microarchitecture enhancements, memory hierarchy, and dataflow, and develop validation models and demos to

Qualifications

• Master's or PhD in Engineering, Electronics and Communication, Electrical, Computer Science, or related field.
• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.

Responsibilities

• Analyze, design, and optimize ML 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.

Skills

DSP architecture

embedded NPU design

low-power AI systems

performance analysis

benchmarking

optimization

Embedded C/C++

Python

RTOS

hardware/software co-design

Power modeling

Education

Master's or PhD in Engineering/EC/CS

Bachelor's in Engineering/CS/IS

Tools

Embedded C/C++

Python

RTOS

ML tooling

Job description

Company:

Qualcomm India Private Limited

Job Area:

Engineering Group, Engineering Group > Systems Engineering

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

• 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.

Applicants: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail [e-mail] or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.