Senior Deep Learning Systems Architect

Responsibilities

• 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.
• What we need to see
• 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…
• Programming fluency with C++ and ideally Python.
• Work experience with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP) is a huge plus.

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