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# Senior Deep Learning Systems Architect

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $224,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Nvidia CUDA, Computer Programming, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Machine Learning, OpenMP, OpenCL, Tensorflow, Systems Architecture, Graphics Processing Unit (GPU), Pytorch, Deep Learning, Information Technology
- **Published:** July 16, 2026
- **Apply:** https://www.juju.com/job/00000000gh6gkc

## About the Role

+ MS (or equivalent experience) or PhD degree in computer science, computer architecture, electrical engineering or related field with 10+ years of relevant work experience. Additional equivalent experience in several of the relevant areas listed below can substitute for an advanced degree.

 + Strong background in at least a few of the following relevant areas is required in your work history: Machine learning (with focus on Deep Neural Networks), including a solid understanding of DL fundamentals; Experience adapting and training DNNs for various tasks; Experience developing code for one or more of the DNN training frameworks (such as PyTorch, TensorFlow or JAX): Numerical analysis, Performance analysis and optimization & Computer architecture.

 + Programming fluency with C++ and ideally Python.

 + Work experience with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP) is a huge plus.

## Description

+ As a member of our deep learning architecture team, you will contribute to features that help next-generation GPUs and systems advancing 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.

 + As a system architect for NVIDIA's offerings for AI systems, you will participate in engineering projects and co-design architecture for systems from conception, specification and prototyping.

 + Understanding various AI/DL workloads and their mapping to underlying HW and Systems. Identifying potential improvements and bottlenecks, proposing solutions to address existing gaps, and accelerate/improve current systems/methods.

 + Comprehensive analyses from first principles of various deep learning techniques, system optimizations to build out analytical models as well as implementing prototypes, and benchmarking to test/prove ideas.


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