Sr Engineer: Embedded AI Architecture and Systems Engineer

Technology, Data & Digital · Software & Web Development · Software Engineering · Systems Engineering · Machine Learning

In short

Qualcomm is seeking a Sr. Engineer for its Audio and Low-Power AI Architecture group to design, analyze, and optimize DSP and embedded NPU performance for Snapdragon platforms. This role involves architectural analysis, performance modeling, and deployment of machine learning software for efficient on-device AI across various use cases and product tiers.

Responsibilities

Analyze, design, and optimize Machine learning kernels on ML HW accelerator for performance, power, and area efficiency.
Conduct architectural analysis and benchmarking of ML subsystems, identifying bottlenecks and proposing solutions.
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 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.
Document architectural analysis, optimization strategies, and performance results.

Requirements

Solid background in DSP architecture, embedded NPU design, and low-power AI systems.
Proven experience in performance analysis, benchmarking, and optimization on 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.

Desired Qualifications

Master’s or PhD degree 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.
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.

Skills

Embedded C/C++PythonMachine LearningDSPNPU

#Embedded AI#Architecture#Systems Engineering#DSP#NPU#Machine Learning#Snapdragon#Performance Optimization#Power Efficiency#On-device AI#Hyderabad#Qualcomm