Senior Deep Learning Systems Architect

NVIDIA•Santa Clara, CA

7d•$224,000 - $356,500

About The Position

We are now looking for a Senior Deep Learning Systems Architect! NVIDIA is seeking architects like you 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. This position offers you the opportunity to have a real impact in a dynamic, technology-focused company. What you'll be doing: 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.

Requirements

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.

Nice To Haves

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

Responsibilities

Contribute to features that help next-generation GPUs and systems advancing the state of AI.
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.
Participate in engineering projects and co-design architecture for systems from conception, specification and prototyping.
Understand various AI/DL workloads and their mapping to underlying HW and Systems.
Identify potential improvements and bottlenecks, proposing solutions to address existing gaps, and accelerate/improve current systems/methods.
Perform comprehensive analyses from first principles of various deep learning techniques, system optimizations to build out analytical models.
Implement prototypes and conduct benchmarking to test/prove ideas.