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Lead HPC Systems & Performance Engineer

Job in Dallas, Dallas County, Texas, 75215, USA
Listing for: Stanford Black Limited
Full Time position
Listed on 2026-09-04
Job specializations:
  • IT/Tech
    Systems Engineer, Hardware Engineer
  • Engineering
    Systems Engineer, Hardware Engineer, Test Engineer
Salary/Wage Range or Industry Benchmark: 140000 - 210000 USD Yearly USD 140000.00 210000.00 YEAR
Job Description & How to Apply Below

Lead HPC Systems & Performance Engineer (Dallas, Texas):

The Company:

My client is a rapidly growing HPC and AI Supercompute Lab operating at the forefront of hyperscale compute.

With significant investment behind its growth, the business is building next-generation infrastructure supporting demanding AI, scientific research, simulation, and data-intensive workloads.

The Role:

They are looking for a Lead HPC Systems & Performance Engineer to take ownership of compute performance across large-scale HPC environments.

You'll evaluate emerging CPU, GPU and accelerator technologies
, benchmark new hardware, identify performance bottlenecks, and turn real-world test data into architecture decisions.

Working across the compute stack, you'll collaborate with storage and networking specialists, hardware vendors, internal engineering teams and customers to build and optimise end-to-end HPC platforms.

Required Skills:

  • Masters &/or PhD in Computer Science, Systems Engineering, or similar subject(s).
  • Strong background in HPC systems, performance engineering or systems architecture
    .
  • Deep understanding of CPU, GPU and accelerator architectures
    .
  • Strong knowledge of memory hierarchy, NUMA and system performance
    .
  • Hands-on experience with Linux tuning, optimisation and performance profiling
    .
  • Experience designing or optimising HPC clusters and parallel computing environments
    .
  • Understanding of Infini Band/RoCE and their impact on compute performance.
  • Experience benchmarking and evaluating new hardware.
  • Knowledge of distributed/parallel storage and its relationship with compute performance.
  • Experience supporting demanding AI/ML, scientific computing or simulation workloads
    .
  • NVIDIA GPU ecosystem and tools such as DCGM, Nsight or MLPerf
    .
  • Experience with large-scale GPU/CPU/DPU clusters & emerging/pre-production hardware.
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