Senior Solution Engineer, Compute Systems

Nvidia

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
Santa Clara, CAWestford, MAAustin, TXDurham, NCRedmond, WA
Salary
$168,000–$270,250 / yr
Posted
25 days ago
Freshness
Confirmed live 2 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $193k
This role $219k
$136k most similar roles pay here $285k

This role pays more than 68% of similar roles. Most pay $151,000–$235,750 — the shaded band above. At the midpoint, this role pays about $219k versus about $193k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Senior Solution Engineer, Compute Systems

As a Senior Solution Engineer, Compute Systems on the NVEX Solutions Engineering team, you will provide direct support to enterprise customers using GPU accelerated platforms like DGX, HGX, and MGX. You will triage hardware platform issues and AI/ML workloads in large datacenters of rack-scale platforms while contributing to products and software tooling. Your daily work involves managing customer cases from inception to resolution, providing logs for engineering teams, and documenting interactions to enhance the knowledge base. The role requires technical expertise in Linux, C/C++, Python, and multi-GPU platforms. You will utilize tools such as Docker, Kubernetes, or Slurm while applying agentic AI skills to solve problems. Key domain experience includes system software like firmware, BIOS, and kernels, alongside distributed GPU-accelerated workloads, CUDA, and high-performance computing technologies like NCCL and MPI.

What you'll do

  • Provide direct technical support to enterprise customers to resolve issues on GPU-accelerated platforms like DGX and HGX.
  • Triage hardware platform issues and AI/ML workloads in large-scale data centers.
  • Collaborate with engineering teams by providing logs, reproduction steps, and triage information for complex cases.
  • Develop and maintain internal tools and products using agentic AI and Python programming.
  • Manage the full lifecycle of customer issues from initial inception to final resolution.
  • Document customer interactions to improve the company's internal knowledge base.
  • Optimize and customize Linux environments specifically for AI/ML workloads.
  • Analyze performance of distributed GPU-accelerated workloads across multi-GPU platforms.

What we're looking for

  • Bachelor of Science in Computer Engineering, Electrical Engineering, or equivalent experience.
  • At least 10 years of engineering experience with multi-GPU platforms.
  • Expertise in system software including firmware, BIOS, kernel, drivers, and operating systems.
  • Strong ability to troubleshoot, optimize, and customize Linux environments for AI/ML workloads.
  • Experience with containerized solutions using Docker, Kubernetes, and/or Slurm.
  • Proficiency in C/C++ programming of platform OS, firmware, BIOS, kernel, and drivers.
  • Proficiency in Python programming to build custom tools.
  • Professional communication skills and ability to manage multiple projects simultaneously.
  • Experience with parallel programming or GPU acceleration (e.g., CUDA) (preferred).
  • Experience developing in GPU accelerated, cloud, or virtualized environments (preferred).
  • Experience analyzing software performance of distributed workloads (preferred).
  • Knowledge of clustering or HPC data center technologies including NCCL and MPI (preferred).

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