Deep Learning Computer Architect - New College Grad 2026

NVIDIA•Redmond, CA

7d•Hybrid

About The Position

NVIDIA is seeking architects to help design hardware accelerator and processor architectures that enable state-of-the-art machine learning and data analytics algorithms and applications on our next-generation mobile, embedded, and datacenter platforms. As a member of our deep learning architecture team, you will contribute to features that help next-generation GPUs advance the state of AI. This position requires you to keep up with the latest DL research and collaborate with diverse teams (internal and external to NVIDIA), including DL researchers, hardware architects, and software engineers. Your day-to-day work will include analyzing the behavior of various deep learning methods, proposing new features to accelerate or enable various methods, and studying the benefits of the proposed features. Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. The era of AI has begun. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you! Come, join our DL Architecture team and help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field.

Requirements

MS or PhD degree in computer science, computer architecture, electrical engineering or related field or equivalent experience.
2+ years of relevant experience in at least a few of the following relevant areas is required in your work history: Computer architecture , including GPU and system level architecture ; Performance analysis and optimization; Experience with LLM workloads, including performance tuning considerations such as parallelization and fusion strategies; Experience with core deep learning kernels such as matrix multiply , attention, and communication convolution
Programming fluency with C++ and ideally Python
Experience with GPU computing (CUDA)
Experience with deep learning frameworks like PyTorch

Responsibilities

Contribute to features that help next-generation GPUs advance the state of AI.
Keep up with the latest DL research.
Collaborate with diverse teams (internal and external to NVIDIA), including DL researchers, hardware architects, and software engineers.
Analyze the behavior of various deep learning methods.
Propose new features to accelerate or enable various methods.
Study the benefits of the proposed features.