GenAI & High Performance Computing (HPC) Delivery Engineer

Dell Technologies

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
TX
Salary
$122,400–$158,400 / yr
Posted
2 days ago
Freshness
Confirmed live yesterday
Closes
Oct 26, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $205k
This role $140k
$108k $261k
below market most similar roles pay here above market

This role pays less than 86% of similar roles. Most pay $163,125–$246,150 — the blue band above. At the midpoint, this role pays about $140k versus about $205k for comparable roles.

Based on 240 similar postings.

Employer

About Dell Technologies

Dell Technologies is a US-based technology company that designs and sells PCs, servers, storage and networking hardware, along with related software and IT services. Its product lines include the XPS, Latitude and Alienware PCs and the PowerEdge server and PowerStore storage families.

Dell Technologies currently has 95 open roles on FindRole.

Listed pay typically runs $172,000–$228,800 across 89 roles with salary data.

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At a glance

TL;DR · GenAI & High Performance Computing (HPC) Delivery Engineer

The GenAI & High Performance Computing (HPC) Delivery Engineer joins the Service Delivery Team to build, integrate, and test large-scale multi-GPU systems for enterprise customers. This hands-on role involves deploying and configuring GPU-accelerated compute clusters for AI, ML, and HPC workloads. Key responsibilities include performing multi-node testing and benchmarking, validating GPU interconnects and fabric health, and troubleshooting hardware or network issues during onsite deployments. The candidate must utilize technologies such as NVIDIA Base Command Manager, HPL, STREAM, NCCL, RCCL, and OSU Microbenchmarks. Required skills include expertise in Linux operating systems like RHEL, Ubuntu, and Rocky, as well as containerization platforms like Docker, Singularity, Slurm, and Kubernetes. The role specifically addresses the technical challenge of optimizing high-speed GenAI and HPC fabrics using InfiniBand and RoCE.

What you'll do

  • Deploy, configure, and validate GPU-accelerated compute clusters for AI, ML, and HPC workloads.
  • Perform multi-node testing and benchmarking using tools like HPL GPU, STREAM, NCCL, and OSU Microbenchmarks.
  • Validate GPU interconnects and fabric health while troubleshooting hardware and network issues during deployment.
  • Configure, harden, and optimize Linux operating systems specifically for GenAI and HPC environments.
  • Produce detailed as-built documentation and performance verification reports for customers.
  • Provide onsite technical support and deployment services to enterprise customers across the United States.

What we're looking for

  • 5+ years of experience with HPC or GenAI clusters, GPU-based systems, AI infrastructure, or related technical environments.
  • Proven hands-on experience with GPU deployment, system configuration, and multi-node testing using NVIDIA Base Command Manager (BCM).
  • Practical experience executing cluster benchmarking tools including HPL, STREAM, NCCL, RCCL, MxP, and OSU Microbenchmarks.
  • Red Hat certification (RHCSA) or 5+ years of relevant Enterprise Linux (RHEL/Rocky/Ubuntu) systems administration experience.
  • Solid understanding of high-speed GenAI/HPC fabrics and networking such as InfiniBand and/or RoCE.
  • Hands-on experience with containerization and workload orchestration platforms like Docker, Singularity/Apptainer, Slurm, or Kubernetes.
  • Excellent technical troubleshooting, communication, and customer-facing skills.
  • Ability to travel up to 70% of the time across the U.S. for deployment projects.
  • Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field (preferred).
  • NVIDIA technical certifications (NCA, NCE, DGX) (preferred).
  • Experience deploying and configuring NVIDIA UFM, InfiniBand, and Spectrum-X fabrics (preferred).
  • Exposure to hybrid cloud or GPU cloud infrastructure (preferred).
  • Experience utilizing GPU observability and performance profiling tools (preferred).

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