NVIDIA
Senior Deep Learning Systems Architect
$224K–$356K
US, CA, Santa Clara · Senior · Full-time · Posted Jul 15, 2026
- Experience
- 10+ years of relevant work experience; additional equivalent experience in several relevant areas can substitute for an advanced degree.
- Education
- MS (or equivalent experience) or PhD degree in computer science, computer architecture, electrical engineering, or a related field.
About the job
Design and analyze hardware and system architectures that accelerate deep learning workloads on next-generation GPU platforms.
What you'll do
- Co-design AI system architectures through conception, specification, and prototyping, collaborating with researchers, hardware architects, and software engineers.
- Analyze deep learning workloads and their mapping to hardware and systems; identify bottlenecks and propose improvements.
- Build analytical models, implement prototypes, and benchmark deep learning techniques and system optimizations.
What they ask for
- MS (or equivalent experience) or PhD in computer science, computer architecture, electrical engineering, or a related field; additional equivalent experience in relevant areas can substitute for an advanced degree.
- 10+ years of relevant work experience.
- Strong background in several listed areas, including deep learning, DNN adaptation and training, DNN training frameworks, numerical analysis, performance analysis and optimization, or computer architecture.
- Programming fluency with C++; Python is described as ideal.
Nice to have
- GPU computing experience (CUDA, OpenCL, OpenACC) and HPC experience (MPI, OpenMP).
Pay
224,000 USD - 356,500 USD (base salary)
Tools
PyTorch, TensorFlow, JAX, C++, Python, CUDA, OpenCL, OpenACC
AI focus
Develop architectures, models, and prototypes to improve systems for deep learning and other AI workloads.
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