GPU Research Engineer- AI (San Diego / Boxborough )

Qualcomm•San Diego, CA

7d•$169,500 - $254,300•Hybrid

About The Position

Qualcomm’s GPU Research Team is seeking innovative GPU architects to advance state-of-the-art capabilities in Artificial Intelligence (AI), Machine Learning (ML), and General-Purpose GPU (GPGPU) computing. This is a unique opportunity to design next-generation GPU architectures that power everything from mobile devices to Windows on Snapdragon (WoS) compute platforms and large-scale data center GPUs. This team is open to location Boxborough as well.

Requirements

Strong understanding of GPU architectures, programming models, and application domains.
Proficiency with APIs such as OpenCL, CUDA, Vulkan, or Direct3D 12.
Solid programming skills in C/C++.
Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 3+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.

Nice To Haves

Familiarity with Python is a strong plus.
Hands-on experience in development, debugging, and optimization of CUDA and OpenCL kernels, as well as GPU compute shaders.
Deep understanding of quantization techniques and data types for large language models (LLMs) and large vision models (LVMs).
Experience designing GPU hardware features for ML/AI acceleration.
Contributions to well-known open-source projects with a proven track record.
Experience with open-source projects such as llama.cpp, vLLM, or similar frameworks is a big plus.

Responsibilities

Collaborate with other GPU architects to design new hardware features and enhance existing GPU architectures for accelerating GPGPU, ML, and AI workloads.
Develop architectural solutions for diverse platforms, including mobile, WoS, and data center GPUs.
Work closely with software teams, hardware design teams, standardization bodies, and internal/external partners to deliver cutting-edge solutions.
Participate in open-source GPGPU/ML/AI projects and contribute to industry-leading initiatives.
Influence the evolution of GPU capabilities for advanced use cases such as large language models (LLMs) and large vision models(LVMs).