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Parallel Computing Jobs in Virginia (NOW HIRING)

Axon

Senior C++ Software Engineer

Sterling, VA · On-site

This position offers the opportunity to deepen your expertise in parallel processing and high-performance computing, with exposure to CUDA programming, positioning you at the forefront of ...

Leidos

GPU Software Engineer

Vienna, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Herndon, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Centreville, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Arlington, VA · On-site

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Alexandria, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Arlington, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Arlington, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Woodbridge, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Annandale, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Fairfax Station, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

Leidos

GPU Software Engineer

Occoquan, VA

$107K - $195K/yr

A solid understanding of GPU programming and parallel computing architectures * Understanding signal processing algorithms written in MATLAB * Parallelization of existing algorithms * Decomposing ...

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All JobsParallel Computing JobsParallel Computing Jobs in Virginia

Parallel Computing information

See Virginia salary details

$24.8K

$51.9K

$89.7K

How much do parallel computing jobs pay per year?

As of Jun 21, 2026, the average yearly pay for parallel computing in Virginia is $51,911.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,700.00 and $59,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Parallel Computing Specialist, and why are they important?

To thrive as a Parallel Computing Specialist, you need strong knowledge of computer architecture, parallel algorithms, and experience with programming languages such as C/C++, Python, and frameworks like MPI or OpenMP, often supported by a degree in computer science or a related field. Familiarity with high-performance computing (HPC) environments, GPU programming (CUDA, OpenCL), and cloud-based parallel processing systems is typically required. Analytical thinking, problem-solving abilities, and effective collaboration are crucial soft skills in this role. These skills are vital for efficiently designing, optimizing, and implementing solutions that leverage parallelism to significantly accelerate computational tasks.

What are some common challenges faced by professionals working in parallel computing roles?

Professionals in parallel computing often encounter challenges such as efficiently dividing complex tasks among multiple processors and minimizing communication overhead between them. Debugging and optimizing performance across parallel architectures can be difficult, as issues like race conditions and load imbalances frequently arise. Additionally, staying current with evolving hardware technologies and parallel programming frameworks is essential to ensure solutions remain efficient and scalable. Collaborating with cross-functional teams, such as data scientists and system architects, is also crucial for integrating parallel solutions into larger projects.

What is the difference between Parallel Computing vs Data Analyst?

AspectParallel ComputingData Analyst
Required CredentialsComputer Science or Engineering degree, programming skillsStatistics, Data Science, or related degree, analytical skills
Work EnvironmentResearch labs, tech companies, high-performance computing centersBusiness, finance, healthcare, corporate offices
Industry UsageTechnology, research, scientific computingBusiness intelligence, market analysis, reporting

While Parallel Computing focuses on developing algorithms to process large data sets efficiently across multiple processors, Data Analysts interpret data to provide actionable insights. Both roles require strong technical skills but serve different purposes: one enhances computational performance, the other informs business decisions.

What is parallel computing?

Parallel computing is a type of computation where many calculations or processes are carried out simultaneously, leveraging multiple processors or computers to solve complex problems more efficiently. It divides large tasks into smaller ones that can be executed concurrently, significantly speeding up processing time. Commonly used in scientific research, data analysis, and engineering, parallel computing is essential for handling large-scale simulations and big data applications.
What are popular job titles related to Parallel Computing jobs in Virginia? For Parallel Computing jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Parallel Computing jobs in Virginia look for? The top searched job categories for Parallel Computing jobs in Virginia are:
Parallel Computing jobs near you
Infographic showing various Parallel Computing job openings in Virginia as of June 2026, with employment types broken down into 82% Full Time, and 18% Part Time. Highlights an 74% Physical, 6% Hybrid, and 20% Remote job distribution, with an average salary of $51,911 per year, or $25 per hour.
HPC Support Engineer

HPC Support Engineer

Science Applications International Corporation

Charlottesville, VA • On-site

Other

Posted 27 days ago

SAIC rating

7.8

Company rating: 7.8 out of 10

Based on 78 frontline employees who took The Breakroom Quiz

69th of 204 rated it services

Job 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
     

The system will be used to support scientific computing, simulation workloads, and other distributed compute operations within a secure research environment.

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

Candidates should have experience working with distributed compute workloads and Linux-based HPC environments.

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 understand job submission workflows and resource allocation concepts such as CPU, memory, and GPU scheduling.


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
     

Experience with RHEL-based environments is preferred.

Distributed Compute Workloads

Experience supporting distributed workloads utilizing parallel computing frameworks such as:

  • MPI

  • OpenMP

Experience supporting the compilation and execution of scientific or engineering applications within Linux HPC environments.
 

Familiarity with common HPC programming languages and compiler toolchains including:

  • C/C++

  • Fortran

Candidates should understand how compiled applications interact with scheduler configuration, compute resources, cluster networking, and distributed runtime environments.

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


Automation and Operational Tooling


Experience writing scripts or tooling using languages such as:

  • Bash
  • Python
     

Automation experience supporting workload execution or operational tasks is beneficial.
 

SAIC is a premier mission integrator focused on advancing the power of technology and innovation to serve and protect our world. Our robust portfolio of offerings across the defense, space, intelligence, and civilian markets includes secure high-end solutions in mission IT, enterprise IT, engineering services, and professional services. We integrate emerging technology, rapidly and securely, into mission critical operations that modernize and enable critical national imperatives.

We are approximately 23,000 strong; driven by mission, united by purpose, and inspired by opportunities. SAIC is an Equal Opportunity Employer. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $7.3 billion. For more information, visit saic.com. For ongoing news, please visit our newsroom.

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.
     

Candidates with the following experience are strongly preferred:

  • 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
     

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