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NVIDIA

Senior Solution Engineer, System Diagnostics Software

Reposted 11 Days Ago
Be an Early Applicant
In-Office
4 Locations
136K-265K Annually
Senior level
In-Office
4 Locations
136K-265K Annually
Senior level
The role involves supporting NVIDIA's GPU platforms by resolving customer issues, assisting engineering teams, and developing tools for AI workloads.
The summary above was generated by AI

NVIDIA is looking for an engineer who wants the excitement of direct customer interaction, and the reward of contributing to software and products, to join our team of Solution Engineers supporting the NVIDIA’s GPU accelerated platforms in AI Factories! You will be working directly with customers to get them solutions on the latest NVIDIA platforms including the GB200. We are looking for an experienced engineer to triage customers' hardware platform issues and AI/ML workloads in huge datacenters of rack-scale platforms, solve customer problems, and contribute to products and software tooling. You must have excellent problem-solving abilities, communication skills and be able to work on multiple projects and tasks. You must be technically strong in Linux, have solid programming skills, and experience with multi-GPU platforms.  Expertise analyzing performance of distributed GPU-accelerated workloads is a plus.

What you'll be doing:

  • Provide direct support to our NVIDIA Enterprise customers and work to answer questions, reproduce, resolve, or advance customer issues.

  • Work with engineering teams on customer issues, providing logs, reproduction information, and other triage information.

  • Create/update product and/or support tools.

  • Take ownership and drive customer issues from inception to resolution.

  • Document customer interactions and better enhance our knowledge base.

  • Develop features and tools as part of solution engineering efforts to support NVIDIA technologies

  • Occasional work on weekends and holidays to support customers
     

What we need to see:

  • Minimum of a BS in Computer Engineering, Electrical Engineering, or equivalent experience.

  • At least 5+ years of engineering experience with multi-GPU platforms

  • Strong system software (firmware, BIOS, kernel, driver, operating system) expertise

  • Solid understanding of Linux and the ability to analyze, optimize, and customize Linux environments for AI/ML workloads.

  • Containerized solutions experience with Docker, Kubernetes, Slurm

  • Professional-level communication skills, including adjusting communication to the technical level of the audience, and staying calm and focused in negative situations.

  • Excellent follow-up and organizational skills, with a passion or love for solving problems.

  • Proficient in C/C++ programming of platform OS, firmware, BIOS, kernel, drivers

  • Proficient in Python programming with the ability to build custom tools
     

Ways to stand out from the crowd:

  • Background with parallel programming or GPU acceleration (e.g., CUDA)

  • Experience developing in GPU accelerated / cloud / virtualized environments

  • Experience analyzing software performance of distributed workloads

  • Clustering or HPC data center technologies including Upper Layer Protocols (NCCL, MPI)

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD for Level 3, and 168,000 USD - 264,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 19, 2025.NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Top Skills

C/C++
Cuda
Docker
Kubernetes
Linux
Mpi
Nccl
Python
Slurm

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