Entry Level & Senior Software Engineer, AI Software Platform (Onsite)
Qualcomm
- Location
- San Diego, California, United States of America
- Work model
- On-Site
- Level
- Senior
- H-1B history
- 22 approvals (FY2023)
- Posted
- 2d ago
Skills
About this role
Company: Qualcomm Technologies, Inc. Job Area: Engineering Group, Engineering Group > Machine Learning Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drive digital transformation, creating a smarter, connected future for all. As an AI Software Core Software Engineer, you will develop and implement cutting-edge machine learning techniques that enable the efficient utilization of state-of-the-art solutions across various technology verticals. In this role, you will be responsible for developing tools and supporting AI SW platform for the Qualcomm AI Stack spanning the Qualcomm AI Runtime (QAIRT) SDK, specifically Core software including Generative AI Inference Extensions (Genie) and the corresponding HW ML accelerator back-end support on the SoC. You will be responsible for researching state-of-the art AI toolchains, performance / memory and HW (CPU/NPU/eNPU) profiling for Edge-AI use cases on the embedded SoC. You will have the opportunity to demonstrate your passion for software tools (developed in house and/or bring in off-the-shelf AI tools), support AI SW ecosystem across development and test and customer support. You will contribute to software toolchains and system level performance / memory profiling across a wide variety of OSes and platforms. You will collaborate with cross-functional teams to deliver robust, scalable AI software solutions, and contribute to a culture of technical excellence, knowledge sharing, and continuous improvement within the AI Software team. Minimum Qualifications: • Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field.
Responsibilities
You will be involved in developing in-house software tools or research off-the-shelf 3rd party toolchains, compilers required for the Qualcomm AI Stack SDKs, specifically QAIRT and Genie, to support the execution of the latest generative AI models on Snapdragon platforms for various AI use cases across a wide-variety of SoCs and OS platforms (Android, Linux, QNX, Windows, Zephyr, etc.) You will be involved in System level performance and memory profiling/benchmarking required to support high performance and efficient AI SW stack on SoCs supporting Edge-AI use cases using Qualcomm’s AI SW SDK and associated components/toolchains Validate, analyze, and optimize the performance and accuracy of software through detailed testing of machine learning use cases. Debug complex issues, perform root cause analysis, and ensure high system reliability. Collaborate with cross-functional teams to deliver robust, scalable toolchains Contribute to a culture of technical excellence, knowledge sharing, and continuous improvement within the AI Software team. Participate in design and code reviews. Work independently with minimal supervision.
Preferred Qualifications
(Desired) Master’s degree in Computer Science, Computer Engineering, or Electrical Engineering. 1+ years of experience building embedded software applications. Proficiency in software tools development using C/C++/Python, compiler toolchains (LLVM, GNU) and build systems (Make/CMake) Projects or Internships experience (Entry level) / 2+ years (Senior) of general software development experience. Experience with development in different OS/platforms like Android, Linux, Windows Knowledge of low-level interactions between operating systems (e.g., Linux, Android, Windows, QNX, etc.) and hardware. Experience using/integrating Qualcomm AI Stack products (e.g., QNN,