Senior Software Developer, AI Networking

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +5 · Remote

Senior Software Developer focused on AI Networking, developing communication frameworks, production tools, and benchmarking infrastructure for large-scale AI training and inference systems. The role involves full-stack benchmarking, automation, and performance analysis across hardware and software stacks, with a focus on ensuring optimal performance of AI workloads on supercomputers and data centers.

What you'd actually do

  1. Developing AI networking communication frameworks and applications running in production on the world’s largest supercomputers and data centers.
  2. Develop production tools and benchmarks used by multiple teams inside and outside NVIDIA.
  3. Enable new AI models within our benchmarking infrastructure and deliver insights through end-to-end analysis of large-scale workloads across hardware and software stacks.
  4. Design and implement automation systems, including large-scale parameter search to identify optimal configurations across complex systems.
  5. Collaborate closely with networking and hardware teams to co-design new features and software interfaces in a fast-paced, evolving environment.

Skills

Required

  • B.Sc., M.Sc degree in Computer Science / Software engineering or equivalent experience.
  • 5+ years of experience.
  • Professional Python development experience.
  • Solid Linux expertise
  • Ability to work across a broad and evolving stack

Nice to have

  • Knowledge and/or experience with modern AI ecosystem: PyTorch, LLMs, inference and training.
  • Familiarity with cluster orchestration systems such as Slurm or Kubernetes.
  • Knowledge in MPI and HPC, InfiniBand, Ethernet and Networking.
  • Experience in performance optimizations

What the JD emphasized

  • Professional Python development experience.
  • build maintainable, long-lived tools that do not impose a heavy burden on the team in terms of maintenance.

Other signals

  • Develop production tools and benchmarks used by multiple teams inside and outside NVIDIA.
  • Enable new AI models within our benchmarking infrastructure and deliver insights through end-to-end analysis of large-scale workloads across hardware and software stacks.
  • Design and implement automation systems, including large-scale parameter search to identify optimal configurations across complex systems.