Senior Software Engineer - Storage

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +3 · Remote

Software Engineer role focused on designing, building, and operating exascale infrastructure for AI research and development at NVIDIA. The role involves managing distributed systems, large-scale storage, compute orchestration, and automation to support AI workloads across thousands of GPUs and petabytes of storage.

What you'd actually do

  1. Design, develop, and operate distributed systems that manage data, compute, and networking for large-scale AI workloads.
  2. Build software and automation to orchestrate workloads across thousands of GPUs and petabytes of storage in multi-region clusters.
  3. Collaborate with AI/ML research teams to understand their requirements and translate them into scalable, high-performance solutions.
  4. Drive improvements in system reliability, performance, and observability to meet exascale standards.
  5. Partner with security, networking, and platform teams to ensure that MARS infrastructure meets the highest standards of robustness and compliance.

Skills

Required

  • BS or equivalent experience in Computer Science, Computer Engineering, or a related technical field.
  • 5+ years of experience developing and operating large-scale distributed systems, infrastructure platforms, or HPC environments.
  • Strong programming skills in C++, Python, or Go, with proven experience designing production-quality software systems.
  • Solid understanding of distributed systems principles, data management, and large-scale orchestration frameworks.
  • Hands-on experience with high-performance storage (e.g., Lustre, GPFS, BeeGFS) and compute scheduling and orchestration (e.g., Slurm, Kubernetes, LSF).
  • Familiarity with cloud environments (Azure, AWS, GCP) and infrastructure automation tools.
  • Strong problem-solving skills, ownership mindset, and the ability to thrive in a fast-paced, collaborative environment.
  • Excellent communication skills and a track record of cross-functional collaboration.

Nice to have

  • Graduate degree (MS/PhD or equivalent experience) in Computer Science, Distributed Systems, or a related field.
  • Expertise in large-scale data management, cluster scheduling, or workload orchestration at exascale scale.
  • Experience building or maintaining infrastructure for AI/ML research, including distributed training pipelines using PyTorch, JAX, or NeMo.
  • Familiarity with data security, compliance, and lifecycle management for research-scale datasets.
  • Demonstrated leadership in system architecture design, performance optimization, or reliability engineering.

What the JD emphasized

  • large-scale distributed systems
  • large-scale storage
  • compute orchestration
  • AI research
  • exascale

Other signals

  • operates exascale infrastructure
  • powers AI research and development at unprecedented scale
  • distributed systems
  • large-scale storage and compute orchestration
  • end-to-end automation
  • thousands of GPUs
  • petabytes of storage