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GPU Research Engineer – AI (San Diego / Boxborough)
AI Research Scientist
• On-site
• Full-time
•
San Diego, USA
Qualcomm's GPU Research Team is seeking innovative GPU architects to advance state-of-the-art capabilities in AI, ML, and general-purpose GPU (GPGPU) computing. This role designs next-generation GPU architectures spanning mobile devices, Windows on Snapdragon compute platforms, and large-scale data center GPUs.
Responsibilities
- ▹Collaborate with other GPU architects to design new hardware features and enhance architectures for 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 partners
- ▹Participate in open-source GPGPU/ML/AI projects
- ▹Influence the evolution of GPU capabilities for advanced use cases such as LLMs and large vision models (LVMs)
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++
- ▹Familiarity with Python is a strong plus
Nice to have
- ▹Hands-on experience developing, debugging, and optimizing CUDA and OpenCL kernels and GPU compute shaders
- ▹Deep understanding of quantization techniques and data types for LLMs and LVMs
- ▹Experience designing GPU hardware features for ML/AI acceleration
- ▹Contributions to well-known open-source projects
- ▹Experience with open-source projects such as llama.cpp, vLLM, or similar frameworks
Soft skills
Teamwork with other GPU architects and cross-functional partnersInnovative, research-oriented thinkingTracking and responding to industry trends
What we offer
- ▹Competitive annual discretionary bonus program
- ▹Annual RSU grant opportunity
- ▹Highly competitive benefits package
About the company
Qualcomm's GPU Research Team develops next-generation GPU architectures for platforms ranging from mobile devices to data center GPUs, with a focus on accelerating AI and ML workloads.
Education: BSc/MSc mérnöki, számítástechnikai vagy villamosmérnöki terület
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