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Gpu Jobs in Texas (NOW HIRING)

Optimize end-to-end GPU performance for real-time autonomous driving workloads , including sensor processing (e.g., camera, LiDAR) and neural network inference. * Develop and optimize parallel ...

Optimize end-to-end GPU performance for real-time autonomous driving workloads , including sensor processing (e.g., camera, LiDAR) and neural network inference. * Develop and optimize parallel ...

GPU Programmer - Remote

Dallas, TX · Remote

$60 - $85/hr

GPU Programmer - Remote Job Type: Contractor Location: Remote Job Overview We are seeking experienced GPU Programmers / Software Engineers to design and optimize GPU-based tasks for AI and LLM ...

GPU Programmer - Remote

Austin, TX · Remote

$60 - $85/hr

GPU Programmer - Remote Job Type: Contractor Location: Remote Job Overview We are seeking experienced GPU Programmers / Software Engineers to design and optimize GPU-based tasks for AI and LLM ...

GPU Programmer - Remote Job Type: Contractor Location: Remote Job Overview We are seeking experienced GPU Programmers / Software Engineers to design and optimize GPU-based tasks for AI and LLM ...

GPU Programming Expert - Remote Job Type: Contractor Location: Remote Job Overview We are seeking experienced GPU Programmers / Software Engineers to design and optimize GPU-based tasks for AI and ...

GPU Programming Expert - Remote Job Type: Contractor Location: Remote Job Overview We are seeking experienced GPU Programmers / Software Engineers to design and optimize GPU-based tasks for AI and ...

GPU Programming Expert - Remote Job Type: Contractor Location: Remote Job Overview We are seeking experienced GPU Programmers / Software Engineers to design and optimize GPU-based tasks for AI and ...

As a member of the Silicon Technologies GPU team, you will work across functions including Architecture, Power, Performance, Silicon Validation, Thermals and Technology. The job involves analyzing ...

Senior GPU Architect

Austin, TX · Hybrid

$161K - $218K/yr

The Mali™ Graphics Processor is the #1 shipping GPU. You will need to have architecture and hardware/software development skills, together with the capacity for creative thought. In this capacity ...

Senior GPU Architect

Austin, TX · On-site

$161K - $218K/yr

The Mali™ Graphics Processor is the #1 shipping GPU. You will need to have architecture and hardware/software development skills, together with the capacity for creative thought. In this capacity ...

GPU Software Engineer Location: Austin, TX Duration: Long Term Contract Roles and Responsibilities: * As a GPU Software Engineer, you will be equipped to develop GPU IP from the early Architectural ...

As a member of the Silicon Technologies GPU team, you will work across functions including Architecture, Power, Performance, Silicon Validation, Thermals and Technology. The job involves analyzing ...

GPU Software Engineer Location: Austin, TX Duration: Long Term Contract Roles and Responsibilities: * As a GPU Software Engineer, you will be equipped to develop GPU IP from the early Architectural ...

GPU Silicon Prototype Engineer

Austin, TX · On-site

$35.50 - $39.75/hr

Join Apple's GPU team and contribute to the creation of graphics processing technology that powers millions of devices worldwide. As part of our growing team, you'll work on pre-silicon validation of ...

We are searching for a Backend Compiler Engineer for an exciting and fun role in our GPU Software organization. Our Compiler team is responsible for constructing and emitting the highest performance ...

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Gpu information

See Texas salary details

$12

$51

$66

How much do gpu jobs pay per hour?

As of Aug 26, 2026, the average hourly pay for gpu in Texas is $51.19, according to ZipRecruiter salary data. Most workers in this role earn between $50.38 and $60.48 per hour, depending on experience, location, and employer.

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 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 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 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.

How to get into the graphics processing unit industry?

To enter the GPU industry, candidates typically need a strong background in computer engineering, electrical engineering, or computer science, with skills in programming languages like C++ and knowledge of graphics APIs such as DirectX or Vulkan. Relevant experience can be gained through internships, projects, or certifications in hardware design, GPU architecture, or related fields. Staying updated on industry developments and obtaining certifications like NVIDIA's or AMD's developer programs can also enhance job prospects.

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

The most popular types of Gpu jobs in Texas are:

What cities in Texas are hiring for Gpu jobs?

Cities in Texas with the most Gpu job openings:

Infographic showing various Gpu job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 93% Full Time, 3% Part Time, and 3% Contract. Highlights an 77% Physical, 7% Hybrid, and 16% Remote job distribution, with an average salary of $106,470 per year, or $51.2 per hour.

GPU Engineer

Bot Auto

Houston, TX • On-site

Full-time

Posted 22 days ago


Job description

Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
You would collaborate with software engineers, AI researchers, and hardware specialists to develop high-performance solutions that meet the stringent requirements of autonomous driving applications. This is an exciting opportunity to work on next-generation transportation technology and make a meaningful impact on the future of mobility.
Key Responsibilities
  • Optimize end-to-end GPU performance for real-time autonomous driving workloads, including sensor processing (e.g., camera, LiDAR) and neural network inference.
  • Develop and optimize parallel computing algorithms and GPU-accelerated components using technologies such as CUDA.
  • Collaborate with cross-functional teams to design and improve onboard GPU software architectures that meet the computational requirements of perception, planning, and control modules.
  • Profile and analyze bottlenecks across GPU computation, memory access, data movement, synchronization, and CPU-GPU interaction.
  • Debug and optimize GPU-based software to improve latency, throughput, resource utilization, and runtime stability on embedded platforms.
Qualifications:
Required:
  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
  • Strong knowledge of parallel computing principles, GPU architecture, memory hierarchy, and performance optimization techniques.
  • Experience profiling GPU applications using tools such as NVIDIA Nsight Systems, Nsight Compute, or equivalent tools.
  • Experience deploying or optimizing neural network inference workloads using technologies such as PyTorch, ONNX, and TensorRT.
  • Experience with real-time embedded systems and handling large data streams from sensors (camera, LiDAR, radar).
  • Strong proficiency in C/C++ and Python.

Preferred:
  • 3+ years of experience in GPU programming and optimization (e.g., CUDA, OpenCL, Vulkan).
  • Experience with NVIDIA Jetson Thor, NVIDIA DRIVE Thor, or similar embedded GPU platforms.
  • Experience with model quantization, including FP8 and NVFP4.
  • Experience managing concurrent GPU workloads and resource isolation using technologies such as NVIDIA Multi-Process Service (MPS), Multi-Instance GPU (MIG), or other related technologies.
  • Experience with GPU-accelerated sensor data compression, including camera, LiDAR, or other onboard sensor data.