What are the responsibilities and job description for the HPC Support Engineer position at Jobs via Dice?
Job ID: 2610673
Location: Charlottesville, VA, US
Date Posted: 2026-03-26
Category: Engineering and Sciences
Subcategory: Systems Engineer
Schedule: Full-Time
Shift: Day Job
Travel: No
Minimum Clearance Required: Top_Secret
Clearance Level Must Be Able to Obtain: TS/SCI
Potential for Remote Work: ORA_ON_SITE
Description
SAIC is looking for a highly qualified HPC Support Engineer to support the Army's Golden Dome initiative. The engineer will support users executing workloads within Linux-based High Performance Computing (HPC) cluster environments used for distributed compute workloads, simulation environments, and GPU-enabled processing.
The environment will include:
Candidates should be comfortable working within cluster-scale computing environments where performance, scheduler configuration, and distributed workload execution are critical operational factors.
The HPC Support Engineer will assist users executing computational workloads within HPC cluster environments.
The role focuses on:
Core Technical Capabilities
Candidates should demonstrate capability in most of the following areas.
HPC Workload Execution
Experience supporting execution of distributed workloads on HPC cluster platforms.
Candidates should understand how compute workloads interact with cluster schedulers, compute nodes, and distributed resources.
Workload Scheduling Platforms
Experience executing and troubleshooting workloads using schedulers such as:
Candidates should be comfortable reading and troubleshooting scheduler job submission scripts used to execute distributed workloads.
Linux Systems Usage
Strong Linux experience including:
Distributed Compute Workloads
Experience supporting distributed workloads utilizing parallel computing frameworks such as:
Familiarity with common HPC programming languages and compiler toolchains including:
Experience troubleshooting application build or runtime issues related to compiler configuration, library dependencies, or MPI environments is desirable.
Familiarity with common HPC compiler toolchains such as GCC, Intel, or LLVM-based compilers is desirable.
GPU Compute Workloads
Experience executing or supporting workloads utilizing GPU-enabled compute environments and CUDA frameworks is desirable.
Performance Troubleshooting
Ability to identify issues affecting workload execution including:
Experience writing scripts or tooling using languages such as:
Qualifications
Candidates must meet the following requirements:
Location: Charlottesville, VA, US
Date Posted: 2026-03-26
Category: Engineering and Sciences
Subcategory: Systems Engineer
Schedule: Full-Time
Shift: Day Job
Travel: No
Minimum Clearance Required: Top_Secret
Clearance Level Must Be Able to Obtain: TS/SCI
Potential for Remote Work: ORA_ON_SITE
Description
SAIC is looking for a highly qualified HPC Support Engineer to support the Army's Golden Dome initiative. The engineer will support users executing workloads within Linux-based High Performance Computing (HPC) cluster environments used for distributed compute workloads, simulation environments, and GPU-enabled processing.
The environment will include:
- multi-node Linux compute clusters
- workload scheduling platforms such as Slurm or PBS
- distributed parallel compute workloads utilizing MPI or OpenMP
- GPU-enabled compute resources supporting CUDA-based processing
- high-performance networking technologies including RDMA / InfiniBand
Candidates should be comfortable working within cluster-scale computing environments where performance, scheduler configuration, and distributed workload execution are critical operational factors.
The HPC Support Engineer will assist users executing computational workloads within HPC cluster environments.
The role focuses on:
- supporting distributed compute workloads
- troubleshooting job execution issues
- assisting users with scheduler job submission scripts
- identifying workload performance bottlenecks
- supporting GPU-enabled workloads
- promoting efficient cluster utilization and HPC best practices
Core Technical Capabilities
Candidates should demonstrate capability in most of the following areas.
HPC Workload Execution
Experience supporting execution of distributed workloads on HPC cluster platforms.
Candidates should understand how compute workloads interact with cluster schedulers, compute nodes, and distributed resources.
Workload Scheduling Platforms
Experience executing and troubleshooting workloads using schedulers such as:
- Slurm
- PBS / PBS Pro
- Torque
- Grid Engine
Candidates should be comfortable reading and troubleshooting scheduler job submission scripts used to execute distributed workloads.
Linux Systems Usage
Strong Linux experience including:
- command-line system usage
- execution of compute workloads within Linux environments
- troubleshooting application execution issues
Distributed Compute Workloads
Experience supporting distributed workloads utilizing parallel computing frameworks such as:
- MPI
- OpenMP
Familiarity with common HPC programming languages and compiler toolchains including:
- C/C
- Fortran
Experience troubleshooting application build or runtime issues related to compiler configuration, library dependencies, or MPI environments is desirable.
Familiarity with common HPC compiler toolchains such as GCC, Intel, or LLVM-based compilers is desirable.
GPU Compute Workloads
Experience executing or supporting workloads utilizing GPU-enabled compute environments and CUDA frameworks is desirable.
Performance Troubleshooting
Ability to identify issues affecting workload execution including:
- inefficient resource allocation
- scheduler configuration issues
- application execution failures
- distributed compute performance bottlenecks
Experience writing scripts or tooling using languages such as:
- Bash
- Python
Qualifications
Candidates must meet the following requirements:
- Bachelor degree in science/technology; 4 additional YoE can be substituted for degree
- 8 years of experience is required
- Minimum 5 years of experience working in Linux environments supporting distributed compute workloads or HPC cluster platforms
- An Active Top Secret clearance is required; an active TS/SCI clearance must be obtained prior to beginning work.
- 100% onsite support in Charlottesville, VA
- Experience executing or troubleshooting workloads using HPC workload schedulers such as Slurm, PBS, Torque, or similar systems
- Experience using command-line Linux environments
- Experience with scripting or automation tools (Bash, Python, or similar)
- Ability to obtain required DoD 8140 (8570) IAT Level II certification
- Candidates must have direct experience working with HPC or distributed compute workloads.
- Experience supporting HPC cluster environments used for distributed compute workloads
- Experience executing or troubleshooting MPI or OpenMP workloads
- Experience supporting GPU-enabled workloads and CUDA frameworks
- Experience supporting scientific or engineering compute applications
- Experience supporting research, laboratory, or mission computing environments
- Experience supporting systems within DoD/DoW or IC environments