Nvidia Senior Deep Learning Systems Engineer Datacenters 

United States, California 

19048957

What you'll be doing:

• Help develop software infrastructure to characterize and analyze a broad range Deep Learning applications

• Evolve cost-efficient datacenter architectures tailored to meet the needs of Large Language Models (LLMs).

• Work with experts to help develop analysis and profiling tools in Python, bash and C++ to measure key performance metrics of DL workloads running on Nvidia systems.

• Analyze system and software characteristics of DL applications.

• Develop analysis tools and methodologies to measure key performance metrics and to estimate potential for efficiency improvement.

What we need to see:

• A Bachelor’s degree in Electrical Engineering or Computer Science or equivalent experience (Masters or PhD degree preferred).

• 8 years or more of relevant experience.

• Experience in at least one of the following:

  • System Software: Operating Systems (Linux), Compilers, GPU kernels (CUDA), DL Frameworks (PyTorch, TensorFlow).

  • Silicon Architecture and Performance Modeling/Analysis: CPU, GPU, Memory or Network Architecture

• Experience programming in C/C++ and Python. Exposure to Containerization Platforms (docker) and Datacenter Workload Managers (slurm) is a plus.

• A deep understanding of computer system architecture and performance analysis is essential for success in this role. Applicants should have demonstrated hands-on experience in these domains.

• Demonstrated ability to work in virtual environments, and a strong drive to own tasks from beginning to end. Prior experience with such environments will make you stand out.

Ways to stand out from the crowd:

• Background with system software, Operating system intrinsics, GPU kernels (CUDA), or DL Frameworks (PyTorch, TensorFlow).

• Experience with silicon performance monitoring or profiling tools (e.g. perf, gprof, nvidia-smi, dcgm).

• In depth performance modeling experience in any one of CPU, GPU, Memory or Network Architecture

• Exposure to Containerization Platforms (docker) and Datacenter Workload Managers (slurm).

• Prior experience with multi-site teams or multi-functional teams.