GenAI & High Performance Computing (HPC) Delivery Engineer

Dell Technologies

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
Oklahoma City, OKRemote, OH
Salary
$110,160–$142,560 / 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 $126k
$94k $262k
below market most similar roles pay here above market

This role pays less than 96% of similar roles. Most pay $163,125–$246,150 — the blue band above. At the midpoint, this role pays about $126k 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, configuring, and validating GPU-accelerated compute clusters for AI, ML, and HPC workloads. Key responsibilities include performing multi-node testing and benchmarking using HPL GPU, HPL MxP, STREAM, NCCL, RCCL, and OSU Microbenchmarks. The engineer must troubleshoot hardware, component, and network issues while optimizing Linux operating systems like RHEL, Ubuntu, and Rocky. Required technical skills include experience with NVIDIA Base Command Manager, InfiniBand, RoCE, and containerization platforms such as Docker, Singularity, Slurm, or Kubernetes. The role focuses on ensuring optimal performance of GenAI and HPC infrastructure through rigorous fabric health validation and detailed performance verification reporting.

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 all deployments.
  • Work directly with enterprise customers onsite to support large-scale infrastructure projects across the U.S.

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 (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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