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Parallel Computing Jobs in Virginia (NOW HIRING)

teKnoluxion

HPC Support Engineer

Charlottesville, VA · On-site

Experience with parallel computing frameworks (MPI, OpenMP, or CUDA-based GPU workloads) * Experience supporting scientific or engineering applications requiring large-scale compute resources

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

Arlington, VA · On-site

$107.90K - $195.05K/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

$107.90K - $195.05K/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

$69.55K - $125.73K/yr

An understanding of GPU programming and parallel computing architectures * Grow and develop experience in: * Signal processing algorithms written in MATLAB * Parallelization of existing algorithms

Leidos

GPU Software Engineer

Arlington, VA

$69.55K - $125.73K/yr

An understanding of GPU programming and parallel computing architectures * Grow and develop experience in: * Signal processing algorithms written in MATLAB * Parallelization of existing algorithms

Cymertek

HPC Cloud Performance Engineer

Chantilly, VA

$57.50 - $77/hr

Proficiency in parallel computing * Cloud orchestration tools * HPC workload scheduling * Performance profiling tools * Scripting for automation * Data analysis for performance trends DESIRED SKILLS

Cymertek

HPC Cloud Performance Engineer

Tysons, VA · On-site

$56 - $74.75/hr

Proficiency in parallel computing * Cloud orchestration tools * HPC workload scheduling * Performance profiling tools * Scripting for automation * Data analysis for performance trends DESIRED SKILLS

Cymertek

HPC Cloud Performance Engineer

Reston, VA

$58 - $77.75/hr

Proficiency in parallel computing * Cloud orchestration tools * HPC workload scheduling * Performance profiling tools * Scripting for automation * Data analysis for performance trends DESIRED SKILLS

Cymertek

HPC Cloud Performance Engineer

Reston, VA · On-site

$58 - $77.75/hr

Proficiency in parallel computing * Cloud orchestration tools * HPC workload scheduling * Performance profiling tools * Scripting for automation * Data analysis for performance trends DESIRED SKILLS

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Parallel Computing information

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$24.8K

$51.9K

$89.7K

How much do parallel computing jobs pay per year?

As of May 31, 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 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 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 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 May 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Hybrid job distribution, with an average salary of $51,911 per year, or $25 per hour.
HPC Systems Engineer

HPC Systems Engineer

Science Applications International Corporation

Charlottesville, VA

Other

Posted 5 days ago

Job description

SAIC is looking for a highly qualified HPC Systems Engineer to support the Army's Golden Dome initiative. The engineer will support the deployment and sustainment of 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
  • cluster provisioning frameworks (e.g., xCAT, Warewulf)
  • high-performance networking technologies including RDMA / InfiniBand
  • distributed parallel compute workloads utilizing MPI or OpenMP
  • GPU-enabled compute resources supporting CUDA-based processing

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 Systems Engineer will support the build-out, configuration, and sustainment of HPC cluster platforms.


The role focuses on:

  • cluster platform configuration
  • scheduler administration
  • distributed compute troubleshooting
  • performance analysis across compute, storage, and network layers
  • GPU compute workload support
  • automation and operational tooling

Candidates should have experience working with multi-node Linux cluster environments and distributed compute workloads.

Core Technical Capabilities

Candidates should demonstrate capability in most of the following areas.

HPC Cluster Platforms

Experience supporting multi-node Linux compute clusters, including node integration, configuration, and operational sustainment.

Experience with cluster provisioning tools such as xCAT, Warewulf, or similar node deployment systems is beneficial.

Workload Scheduling Platforms

Experience supporting distributed compute workloads using schedulers such as:

  • Slurm
  • PBS / PBS Pro
  • Torque
  • Grid Engine

Candidates should understand queue configuration, job submission workflows, and scheduler troubleshooting.

Candidates should understand how workload schedulers interact with distributed compute workloads and containerized execution environments.

Linux Systems Administration


Strong Linux administration experience including:

  • command-line system administration
  • server and compute node configuration
  • system troubleshooting in distributed compute environments

Experience with RHEL-based environments is preferred.

Distributed and Containerized Workloads


Experience supporting distributed compute workloads utilizing parallel computing frameworks such as:

  • MPI
  • OpenMP
  • GPU compute frameworks

Candidates should understand how workload schedulers interact with distributed compute workloads and containerized execution environments within HPC clusters.

Familiarity with container technologies commonly used in HPC environments such as:

  • Docker
  • Podman
  • Singularity / Apptainer

Candidates should understand how containerized workloads interact with schedulers, GPU resources, and distributed compute environments.

Experience supporting containerized HPC workloads or integrating container platforms with cluster infrastructure is desirable.

HPC Networking


Familiarity with high-performance networking technologies including:

  • RDMA networking
  • InfiniBand
  • high-throughput cluster networking architectures

Candidates should be comfortable assisting with troubleshooting cluster communication or performance issues.

GPU Compute Environments

Experience supporting GPU-enabled compute environments and workloads utilizing CUDA frameworks is desirable.


Automation and Operational Tooling
Experience writing scripts or operational tooling using languages such as:

  • Bash
  • Python
     

Automation experience supporting system administration or cluster operations 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; 10 additional YoE can be substituted for degree
  • 8+ years of experience is required
  • Minimum 6 years of experience administering Linux systems in enterprise, research computing, or distributed compute environments
  • 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 supporting distributed compute environments or HPC cluster platforms
  • Experience working with workload schedulers such as Slurm, PBS, Torque, or similar systems
  • Experience administering Linux systems through command-line interfaces
  • 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 with HPC or distributed compute environments.
     

Candidates with the following experience are strongly preferred:

  • Administration of multi-node HPC cluster environments
  • Experience with parallel or distributed file systems such as Lustre, BeeGFS, or GPFS
  • Experience supporting GPU-enabled compute environments and CUDA workloads
  • Experience with configuration management tools such as Ansible or Puppet
  • Experience supporting research, laboratory, or mission computing environments
  • Experience supporting systems within DoD/DoW or IC environments
     

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