Core AI Software Engineer (Genie) – Onsite, San Diego

QUALCOMM, Inc.; San Diego (CA); Hybrid; USD 141,000 - 211,000; Full time

Job summary

Qualcomm Technologies, Inc. is seeking an entry-level or senior Software Engineer for Core AI Software (Onsite) in San Diego, CA. You will help design, implement, and optimize the Qualcomm AI Stack features within QAIRT and Genie, enabling efficient inference on Snapdragon platforms.

You will collaborate across teams to improve runtime efficiency, latency, and power usage for AI workloads on low-power accelerators, contributing to a culture of technical excellence.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
• Projects or internships experience (Entry level) / 2+ years (Senior) of general software development experience.
• Proficiency in software development using C/C++.
• Experience with development in a Linux environment.
• Strong software development skills including data structures, algorithms, and debugging.

Responsibilities

• Develop software for the Qualcomm AI Stack SDKs, specifically QAIRT and Genie.
• Design and implement core runtime components for inference on Snapdragon platforms.
• Validate, analyze, and optimize the performance and accuracy of ML software.
• Debug complex issues and perform root cause analysis for high reliability.
• Collaborate with cross-functional teams to deliver robust AI software solutions.
• Assist in feature development and apply ML techniques to products and AI solutions.

Skills

C/C++

Linux

Data structures

Algorithms

OO design

CMake

Education

Bachelor's degree in CS/Engineering/Info Systems or related

Master's degree

PhD

Tools

CMake

Job description

Qualcomm Technologies, Inc. is seeking an entry-level or senior Software Engineer for Core AI Software (Onsite) in San Diego, CA. You will help design, implement, and optimize the Qualcomm AI Stack features within QAIRT and Genie, enabling efficient inference on Snapdragon platforms.

You will collaborate across teams to improve runtime efficiency, latency, and power usage for AI workloads on low-power accelerators, contributing to a culture of technical excellence.