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Gpu Computing Jobs in California (NOW HIRING)

Nvidia

Senior GPU Memory Architect

Santa Clara, CA · On-site

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can ...

Qualcomm

Senior GPU SW Engineer

San Diego, CA · On-site

$148K - $183K/yr

Experience developing GPU computing applications with OpenCL, CUDA, SYCL etc. * Experience debugging, optimizing, profiling, and tuning real-time graphics and/or compute applications * Experience ...

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Your expertise in GPU computing, performance optimization, and parallel programming will be instrumental in driving the development of high-performance, energy-efficient solutions that redefine the ...

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Your expertise in GPU computing, performance optimization, and parallel programming will be instrumental in driving the development of high-performance, energy-efficient solutions that redefine the ...

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Gpu Computing information

See California salary details

$9

$18

$24

How much do gpu computing jobs pay per hour?

As of Jul 26, 2026, the average hourly pay for gpu computing in California is $18.04, according to ZipRecruiter salary data. Most workers in this role earn between $14.95 and $19.47 per hour, depending on experience, location, and employer.

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 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 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.

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 popular job titles related to Gpu Computing jobs in California? For Gpu Computing jobs in California, the most frequently searched job titles are:
What job categories do people searching Gpu Computing jobs in California look for? The top searched job categories for Gpu Computing jobs in California are:
Gpu Computing jobs near you
Infographic showing various Gpu Computing job openings in California as of July 2026, with employment types broken down into 82% Full Time, 15% Part Time, and 3% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $37,518 per year, or $18 per hour.
Research Computing GPU Systems Engineer

Research Computing GPU Systems Engineer

Stanford University

Stanford, CA • On-site

Full-time

Posted 20 days ago

Stanford University rating

7.8

Company rating: 7.8 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

225th of 611 rated colleges and universities

Job description

Job Summary:
Stanford University is seeking a GPU Cluster Lead Engineer to oversee technical operations and strategic development of its NVIDIA SuperPOD. The role involves managing GPU infrastructure, optimizing system performance, and supporting a diverse research community in various fields including AI and computational biology.
Responsibilities:
• 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.
• 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.
• 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.
• 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.
Qualifications:
Required:
• 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)
• 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:
• 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)
Company:
Stanford University is a teaching and research university that focuses on graduate programs in law, medicine, education, and business. Founded in 1885, the company is headquartered in Stanford, USA, with a team of 10001+ employees. The company is currently Late Stage.

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