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GPU Research Engineer – AI (San Diego / Boxborough)

AI Research Scientist • On-site • Full-time United States 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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