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Gpu Jobs in Virginia (NOW HIRING)

Leidos

GPU Software Engineer

Arlington, VA · On-site

$107K - $195K/yr

  • Retirement

  • PTO

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

  • Retirement

  • PTO

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

  • Medical

  • Retirement

  • PTO

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

The Josef Group

AI Infrastructure Engineer

Chantilly, VA · On-site

$110K - $144K/yr

Optimize GPU utilization, memory management, quantization, batching, and capacity planning to balance performance and cost. * Develop and maintain CI/CD pipelines, observability, monitoring, and ...

Introl

Data Center Deployment Foreman I

Dulles, VA · On-site

$98K - $118K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Who we are At Introl, we specialize in large-scale GPU cluster deployments, managing up to 100,000 of the most advanced GPUs available. Our experienced team works with leading-edge technology ...

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Gpu information

What is the difference between Gpu vs Data Scientist?

AspectGpuData Scientist
Required CredentialsKnowledge of parallel computing, programming skills (CUDA, OpenCL)Degree in Computer Science, Statistics, or related fields; programming skills
Work EnvironmentHardware-focused, technical, often in R&D or engineering teamsData analysis, modeling, research in various industries
Industry UsageTech, gaming, AI, machine learningFinance, healthcare, tech, marketing

Gpu specialists focus on hardware and parallel processing for computing tasks, while data scientists analyze data to extract insights. Both roles require technical skills, but Gpu roles are more hardware-oriented, whereas data scientists focus on data analysis and modeling.

What is a GPU engineer?

A GPU job refers to a computing task that utilizes a Graphics Processing Unit (GPU) for acceleration. GPUs are specialized processors designed for parallel processing, making them ideal for tasks like machine learning, scientific simulations, and rendering. Many software applications offload intensive computations to GPUs to improve performance and efficiency. Jobs related to GPUs can involve programming, optimization, and hardware configuration in fields like AI, gaming, and data analysis.

What are the key skills and qualifications needed to thrive as a GPU engineer?

To thrive as a GPU Engineer, you need a solid background in computer engineering, mathematics, and programming languages such as C++ or CUDA, often supported by a relevant degree. Familiarity with GPU architectures, parallel computing frameworks, and tools like OpenCL or Vulkan is typically required. Analytical thinking, problem-solving, and teamwork are essential soft skills for innovating and debugging complex systems. These abilities are crucial for optimizing performance, ensuring compatibility, and driving advancements in graphics and computational workloads.

What is a GPU?

A GPU, or Graphics Processing Unit, is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images and graphics for display. While originally developed for rendering graphics in video games and visual applications, GPUs are now widely used for parallel processing tasks in areas such as artificial intelligence, data science, and scientific computing. Their architecture allows them to handle thousands of operations simultaneously, making them much faster than traditional CPUs for certain workloads.

What are some common challenges faced by GPU engineers when optimizing performance for various applications?

GPU engineers often encounter challenges such as balancing high computational throughput with power efficiency, ensuring compatibility across different hardware architectures, and optimizing code for parallel processing. They must also troubleshoot bottlenecks in memory bandwidth and latency that can impact performance. Collaboration with software developers and hardware architects is crucial to identify and resolve these issues, and staying updated with the latest advances in GPU technologies is essential for continued success.

What are the most commonly searched types of Gpu jobs in Virginia?

The most popular types of Gpu jobs in Virginia are:

What are popular job titles related to Gpu jobs in Virginia?

For Gpu jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Gpu jobs in Virginia look for?

The top searched job categories for Gpu jobs in Virginia are:

Gpu jobs near you
Infographic showing various Gpu job openings in Virginia as of August 2026, with employment types broken down into 94% Full Time, 3% Part Time, 1% Temporary, and 2% Contract. Highlights an 81% Physical, 7% Hybrid, and 12% Remote job distribution.

GPU & Structured Cabling Project Manager

Total Deployment Solutions

Sterling, VA • On-site

Full-time

Medical, PTO

Re-posted 3 days ago

Job description

About Total Deployment Solutions
Total Deployment Solutions (TDS) is a nationwide information technology services provider with 14 years of experience delivering innovative, business-critical solutions. We specialize in AI and GPU deployments, comprehensive data center support, IMAC services - desktop relocation, ensuring organizations transition seamlessly into new or evolving work environments. Our teams manage every detail, from workstation setup to ongoing desktop support, so clients can operate without disruption. Trusted by enterprise and data center partners, TDS continues to lead and adapt to the evolving technology landscape with a focus on reliability, precision, and long-term partnership.
Overview
We are seeking a highly motivated and detail-oriented GPU & Structured Cabling Project Manager to oversee and execute structured cabling projects in high-performance data center environments, with a focus on GPU and AI infrastructure. This role requires frequent travel to project sites across the country and close collaboration with engineering, construction, and IT teams.
Responsibilities:
  • Manage end-to-end structured cabling projects for data centers, including planning, execution, and close-out.
  • Coordinate with internal teams, subcontractors, and vendors to ensure timely and quality delivery.
  • Interpret and implement cabling layouts for GPU and AI workloads, ensuring compliance with industry standards and client specifications.
  • Conduct site surveys, develop scope of work (SOW), and prepare project documentation.
  • Monitor project budgets, timelines, and resource allocation.
  • Ensure adherence to safety protocols and quality assurance standards on-site.
  • Provide regular updates to stakeholders and resolve project-related issues proactively.
  • Travel frequently (up to 75%) to data center locations across the U.S.
Qualifications:
  • 3-5 years of experience in structured cabling project management, preferably in data center environments.
  • Strong understanding of cabling standards (TIA/EIA, BICSI) and best practices.
  • Experience with GPU/AI-specific cabling layouts and high-density environments is highly desirable.
  • Proficiency in reading and interpreting technical drawings and network diagrams.
  • Excellent organizational, communication, and leadership skills.
  • Ability to manage multiple projects simultaneously in a fast-paced environment.
  • PMP or RCDD certification is a plus.
  • Valid driver's license and ability to travel extensively.

Job Type: Full-time
Benefits:
  • Health insurance
  • Paid time off

For more details, visit www.totaldeployment.com.

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