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Gpu Computing Jobs in Texas (NOW HIRING)

Hudson River Trading

GPU Systems Engineer

Austin, TX · On-site

Hudson River Trading (HRT) is looking for GPU Systems Engineers to help scale and evolve our ... We have built one of the world's most sophisticated computing environments for research and ...

Nvidia

Datacenter GPU Power Architect

Austin, TX

We are looking for a Datacenter GPU Power Architect! What you'll be doing: * You will be ... Computing and Visualization. We have some of the most forward-thinking and hardworking people in ...

NVIDIA

Senior Deep Learning Communication Architect

Austin, TX · On-site

$128K - $174K/yr

... GPU computing, including CUDA and OpenCL, and familiarity with InfiniBand and RoCE networks. Preferred : • Prior contributions to one or more DNN training and Inference frameworks as part of your ...

webAI

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... GPU computing, CUDA, and performance profiling. Company : The leader in private AI. Founded in 2020, the company is headquartered in Austin, USA, with a team of 201-500 employees. The company is ...

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How much do gpu computing jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for gpu computing in Texas is $17.03, according to ZipRecruiter salary data. Most workers in this role earn between $14.09 and $18.37 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 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 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 popular job titles related to Gpu Computing jobs in Texas?

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

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

The top searched job categories for Gpu Computing jobs in Texas are:

Gpu Computing jobs near you
Infographic showing various Gpu Computing job openings in Texas as of August 2026, with employment types broken down into 89% Full Time, 9% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $35,418 per year, or $17 per hour.

System Software Engineer, Profiler - GPU

NVIDIA Gruppe

Austin, TX • On-site

$184 - $287.50/hr

Other

Posted 5 days ago

Job description

At NVIDIA, we build groundbreaking products for the following sectors: Automotive, VR, Gaming, Deep Learning, and High Performance Computing. See your efforts in action as developers use your tools to debug, profile and analyze the performance of their systems/applications using the low-level libraries that you helped to craft as a member of the GPU Foundations Developer Tools team! Innovate as you develop the performance analysis capabilities of NVIDIA hardware along with the Nsight tools and the foundation library to support next generation accelerated computing at datacenter scale.

As a system software engineer in the Developer Tools group, you will be developing software that empowers GPU application developers to build outstanding compute applications deployed on the world’s largest distributed environments. We are seeking a talented Software Engineer to join our team and contribute to the performance triage development and co-design of foundational software libraries for developer tools in collaboration with our Hardware Architecture team. Join our team and gain exciting opportunities to work hands‑on at every layer of NVIDIA's outstanding technology.

What you’ll be doing:
  • Design, develop, and maintain GPU performance foundation libraries for Nsight tools with focus on high fidelity hardware events and counters.
  • Develop and implement GPU assembly tests.
  • Build and maintain a test validation framework written primarily in CUDA and GPU Assembly.
  • Utilize emulators to debug and verify instruction events.
  • Document tools use cases and data processing workflows to facilitate architectural explorations.
What we need to see:
  • B.S. EE/CS (or equivalent experience) and 5+ years of experience or MS with 2+ years' experience, or Ph.D.
  • Strong programming ability in C, C++, and scripting languages such as Python.
  • Good understanding or prior experience with low level assembly code.
  • Solid understanding of hardware pipeline and execution unit instruction pipeline concepts, with a willingness to work at a detailed implementation level.
  • Knowledge of hardware-software co-design principles and practices.
  • Experience with performance analysis and optimization of software on hardware accelerators.
  • Experience with developing on simulators and emulators.
  • Excellent problem-solving skills and the ability to work collaboratively in a team environment.
  • Strong communication skills, both written and verbal.
Ways to stand out from the crowd:
  • Shown knowledge of compute (CUDA/OpenCL), modern graphics (DirectX12, OpenGL, Vulkan, Metal), or DL frameworks (PyTorch/JAX).
  • Prior experience authoring developer tools, particularly for GPUs, games, pro visualization, or compute workloads.
  • Knowledge of performance analysis, particularly of GPU applications.
  • Experience in driver development.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 28, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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