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Gpu Computing Jobs (NOW HIRING)

VTG

High-Performance Computing Engineer

Mclean, VA · On-site

The ideal candidate will bring deep technical expertise in large-scale HPC environments, cluster management, GPU computing, and high-speed networking. Do you have what it takes? * Active Top Secret ...

Nvidia

OR

$121.40K - $163.30K/yr

... computing. In the last decade, Python has become the de-facto programming language for ... NVIDIA has been at the forefront of providing GPU-accelerated implementations of the fundamental ...

VTG

High-Performance Computing Engineer

Mclean, VA

The ideal candidate will bring deep technical expertise in large-scale HPC environments, cluster management, GPU computing, and high-speed networking. Do you have what it takes? * Active Top Secret ...

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How much do gpu computing jobs pay per hour?

As of May 31, 2026, the average hourly pay for gpu computing in the United States is $18.28, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $19.71 per hour, depending on experience, location, and employer.

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

To thrive as a GPU Computing Specialist, you need expertise in parallel programming, computer architecture, and a strong foundation in mathematics and algorithms, often supported by a degree in computer science, engineering, or related fields. Familiarity with programming languages like C/C++, CUDA, OpenCL, and experience with GPU hardware and high-performance computing systems are essential. Problem-solving abilities, analytical thinking, and strong collaboration skills help you innovate and work effectively on complex computational projects. These skills ensure efficient development, optimization, and deployment of GPU-accelerated solutions crucial for scientific, engineering, and AI applications.

What are some common challenges faced by GPU Computing professionals when optimizing code for parallel processing?

One of the main challenges in GPU Computing is efficiently restructuring code to leverage the massive parallelism that GPUs offer. Professionals often encounter issues with memory management, synchronization between threads, and minimizing data transfer between CPU and GPU to avoid bottlenecks. Additionally, debugging parallel code can be complex, as errors may not manifest consistently across runs. Collaborating with software engineers, data scientists, and hardware specialists is typical to ensure optimal performance and scalability in real-world applications.

What is GPU computing?

GPU computing refers to the use of a Graphics Processing Unit (GPU) alongside a Central Processing Unit (CPU) to accelerate computational tasks. GPUs are highly efficient at performing parallel operations, making them ideal for complex calculations in fields like machine learning, scientific simulations, and graphics rendering. Unlike traditional CPUs, GPUs can process thousands of threads simultaneously, greatly speeding up tasks that involve large-scale data processing. This makes GPU computing essential in industries requiring high-performance computing solutions.

What jobs make 5000 a week without a degree?

In GPU computing and related tech fields, high-paying roles such as freelance GPU programmer, data scientist, or AI specialist can earn around $5,000 weekly with strong skills and experience. These jobs often require expertise in programming, machine learning, or parallel processing, and may involve contract work or consulting rather than traditional employment.

What is the difference between Gpu Computing vs Data Scientist?

AspectGpu ComputingData Scientist
Required CredentialsKnowledge of GPU architectures, programming skills in CUDA or OpenCLDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentHigh-performance computing environments, data centers, research labsOffice settings, research institutions, tech companies
Industry UsageMachine learning, scientific simulations, graphics renderingData analysis, predictive modeling, business insights

Gpu Computing focuses on leveraging GPU hardware for high-speed processing tasks, often requiring specialized programming skills. Data Scientists analyze data to extract insights, using various tools and statistical methods. While both roles involve data and computing, Gpu Computing is more hardware and performance-oriented, whereas Data Scientists focus on data analysis and modeling.

More about Gpu Computing jobs
What states have the most Gpu Computing jobs? States with the most job openings for Gpu Computing jobs include:
What job categories do people searching Gpu Computing jobs look for? The top searched job categories for Gpu Computing jobs are:
Gpu Computing jobs near you
Infographic showing various Gpu Computing job openings in the United States as of May 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution, with an average salary of $38,016 per year, or $18.3 per hour.
Research Computing GPU Systems Engineer

Research Computing GPU Systems Engineer

Stanford University

Stanford, CA • On-site

Full-time

Medical, Dental, Retirement

Posted 23 days ago

Stanford University rating

7.8

Company rating: 7.8 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

191st of 530 rated colleges and universities

Job description

About the Role
Stanford Research Computing seeks an exceptional GPU Cluster Lead Engineer to oversee technical operations, optimization, and strategic development of Marlowe, Stanford's NVIDIA SuperPOD. This role combines deep technical expertise in GPU computing, large-scale cluster management, and leadership in supporting a diverse research community. You will serve as the technical authority on GPU infrastructure, driving system performance and reliability while enabling groundbreaking research in AI/ML, computational biology, physics, and beyond.
Key Responsibilities
System Operations & Management
  • Lead day-to-day operations of the GPU Cluster, ensuring optimal uptime and performance.
  • Architect monitoring, alerting, and observability solutions using Prometheus, Grafana, DCGM, and Base Command Manager.
  • Manage job scheduling and resource allocation using Slurm, implementing advanced GPU partitioning and configurations.
  • Coordinate maintenance windows, system upgrades, and capacity expansions; lead incident response and root cause analyses.
  • System storage management, optimization, benchmarking and observability reporting.

Performance Optimization & Engineering
  • Design performance tuning strategies for GPU utilization, job throughput, and system efficiency.
  • Optimize NVIDIA GPU fabric configurations including NVLink, NVSwitch, and InfiniBand RDMA networking.
  • Develop containerization strategies using NVIDIA NGC, Docker, and Singularity/Apptainer.
  • Engineer solutions for deep learning frameworks (PyTorch, TensorFlow, JAX) and CUDA application optimization.
  • Benchmark system performance and collaborate with NVIDIA on optimization programs.

User Support & Research Enablement
  • Serve as primary technical consultant for researchers using GPU-accelerated computing,
  • Develop documentation, best practices guides, and training materials; deliver workshops on GPU computing workflows.
  • Profile and optimize user workloads, scaling applications from single-GPU to multi-node distributed training.

Team Leadership & Strategy
  • Mentor junior engineers and contribute to strategic planning for GPU infrastructure expansion.
  • Evaluate emerging GPU technologies and manage vendor relationships with NVIDIA and hardware suppliers.
  • Represent SRC in ongoing interactions with the Stanford Data Sciences group on AI/ML infrastructure; participate in on-call rotation.

Education & Experience
  • Bachelor's degree in Computer Science, Engineering, or related field and ten years of relevant experience or a combination of education and relevant experience.
  • 5+ years in HPC systems administration or research computing; 3+ years managing GPU clusters (NVIDIA A100/H100)

Required Qualifications
  • Expert knowledge of NVIDIA GPU architecture, CUDA, and GPU computing principles (NVLink, MIG, GPUDirect)
  • Advanced Linux administration (RHEL, Ubuntu); expertise with Slurm job scheduler
  • Experience with high-performance networking (InfiniBand, RoCE) and parallel filesystems (Lustre, GPFS)
  • Strong scripting (Python, Bash) and containerization experience (Docker, Singularity, Kubernetes)
  • Familiarity with AI/ML frameworks (PyTorch, TensorFlow) and distributed training techniques
  • Experience with monitoring tools (Prometheus, Grafana) and NVIDIA DCGM

Preferred Qualifications
  • Experience with Base Command Manager or Bright Cluster Manager
  • Background in academic research computing or national lab environments
  • Contributions to open-source HPC or GPU computing projects
  • Knowledge of MLOps practices and GPU virtualization (vGPU, MIG)

Key Competencies
  • Technical leadership
  • Creative problem-solving
  • Excellent communication with technical and non-technical audiences
  • Strong collaboration skills
  • Service-oriented mindset
  • Adaptability to rapidly evolving technology

What We Offer
  • Work with cutting-edge NVIDIA GPU technology enabling groundbreaking research
  • Professional development opportunities
  • Collaborative environment with talented engineers and researchers
  • Comprehensive Stanford benefits package including health, dental, retirement, and education benefits
  • Flexible work arrangements

Physical Requirements*:
  • Constantly perform desk-based computer tasks.
  • Frequently sit, grasp lightly/fine manipulation.
  • Occasionally stand/walk, writing by hand.
  • Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds.

* Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form.
Working Conditions:
  • May work extended hours, evenings, and weekends.

Work Standards:
  • Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
  • Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
  • Subject to and expected to stay in sync with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in Stanford's Administrative Guide, http://adminguide.stanford.edu.

The expected pay range for this position is $190,577 to $200,000 per annum.
Stanford University provides pay ranges representing its good faith estimate of the salary or hourly wage the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.
At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards) provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.
The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned.
Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form.

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