Embedded Low-Power AI/ML Systems Architect

QUALCOMM, Inc.; San Diego (CA); On-site; USD 155,000 - 233,000; Full time

Job summary

Qualcomm Technologies, Inc. seeks an engineer focused on performance optimization, data-path and power analysis for embedded AI subsystems in XR and ambient use cases. You will analyze power-performance trade-offs and drive optimizations across software and hardware for on-device AI.

Responsibilities include optimizing AI workloads on Snapdragon SOCs, enabling power-aware system design, and collaborating across Mobile, XR, Compute, and IoT teams to deliver efficient on-device AI solutions.

Qualifications

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.
• Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.
• PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.

Responsibilities

• Analyze and optimize performance and power of AI systems with focus on always-on AI workloads on Snapdragon SOCs.
• Support system integration, benchmarking, and commercialization of AI solutions across Mobile, XR, Compute, and IoT platforms.
• Perform detailed data-path and memory-access analysis (cache, SRAM, DDR) to identify bottlenecks impacting performance efficiency.

Skills

Embedded ML accelerators

NPUs

Low power design principles

RTOS performance profiling

Python

PyTorch

Executorch

Power modeling

Power analysis

Data movement & bandwidth analysis

Cross-functional collaboration

Education

Bachelor's degree in Engineering/IS/CS

Master's degree in Engineering/IS/CS

PhD in Engineering/IS/CS

Tools

Performance profiling tools

Power measurement tools

Memory/bandwidth analysis tools

Job description

Qualcomm Technologies, Inc. seeks an engineer focused on performance optimization, data-path and power analysis for embedded AI subsystems in XR and ambient use cases. You will analyze power-performance trade-offs and drive optimizations across software and hardware for on-device AI.

Responsibilities include optimizing AI workloads on Snapdragon SOCs, enabling power-aware system design, and collaborating across Mobile, XR, Compute, and IoT teams to deliver efficient on-device AI solutions.