Nvidia Senior Deep Learning Systems Software Engineer - AI Infrastructure 

United States, Texas 

108111837

What you'll be doing:

• Understand, analyze, profile, and optimize deep learning workloads on state-of-the-art hardware and software platforms.

• Build tools to automate workload analysis, workload optimization, and other critical workflows.

• Collaborate with cross-functional teams to analyze and optimize cloud application performance on diverse GPU architectures.

• Identify bottlenecks and inefficiencies in application code and propose optimizations to enhance GPU utilization.

• Drive end-to-end platform optimization from a hardware level to the application and service levels

• Design and implement performance benchmarks and testing methodologies to evaluate application performance.

• Provide guidance and recommendations on optimizing cloud-native applications for speed, scalability, and resource efficiency.

• Share knowledge and best practices with domain expert teams as they transition applications to distributed environments.

What we need to see:

• Masters in CS, EE or CSEE or equivalent experience

• 8+ years of experience in application performance engineering

• Experience using large scale multi node GPU infrastructure on premise or in CSPs

• Background in deep learning model architectures and experience with Pytorch and large scale distributed training

• Experience with application profiling tools such as NVIDIA NSight, Intel VTune etc.

• Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture. Experience with NVIDIA's Infrastructure and software stacks.

• Proven experience analyzing, modeling and tuning DL application performance.

• Proficiency in Python and C/C++ for analyzing and optimizing application code

Ways to stand out from the crowd:

• Strong fundamentals in algorithms and GPU programming experience (CUDA or OpenCL)

• Understanding of NVIDIA's server and software ecosystem

• Hands-on experience in performance optimization and benchmarking on large-scale distributed systems

• Hands-on experience with NVIDIA GPUs, HPC storage, networking, and cloud computing.

• In-depth understanding storage systems, Linux file systems, RDMA networking