Senior Staff ML Engineer - Edge AI & On-Device Systems

QUALCOMM, Inc.; San Diego, Northern (CA, KY); Hybrid; USD 194,000 - 292,000; Full time

Job summary

Qualcomm Technologies, Inc. is seeking a Senior Staff Engineer for on-device ML engineering in San Diego.

You will develop end-to-end embedded AI software to train and finetune models on Qualcomm hardware, optimize ML kernels, and collaborate with hardware teams on new operator implementations for next-generation AI processors. The role emphasizes power-efficient performance, debugging tools, and SDKs to accelerate customer deployments on edge devices such as smartphones and autonomous systems.

Qualifications

• Bachelor's degree or higher in CS, Engineering, IS, or related field.
• Master's degree or PhD with relevant experience considered.
• 6+ years hardware, software or systems engineering experience for Bachelor's; 5+ for Master's; 4+ for PhD.

Responsibilities

• Develop end-to-end embedded AI software to train and finetune neural networks on Qualcomm hardware.
• Design and enhance ML/AI software stack, kernels, and runtime for performance and power efficiency.
• Collaborate with AI Processor Hardware team to implement new ML operators/layers leveraging next-gen AI processors.
• Develop debugging/profiling tools and SDKs to enable rapid deployment of new use cases.

Skills

PyTorch

Python

C

C++

Android

Neural network training

Neural network quantization

QNN SDK

Education

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field

Master's degree in Computer Science, Engineering, Information Systems, or related field

PhD in Computer Science, Engineering, Information Systems, or related field

Tools

QNN SDK

Job description

Qualcomm Technologies, Inc. is seeking a Senior Staff Engineer for on-device ML engineering in San Diego.

You will develop end-to-end embedded AI software to train and finetune models on Qualcomm hardware, optimize ML kernels, and collaborate with hardware teams on new operator implementations for next-generation AI processors. The role emphasizes power-efficient performance, debugging tools, and SDKs to accelerate customer deployments on edge devices such as smartphones and autonomous systems.