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Gpu Engineer Jobs in Texas (NOW HIRING)

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 ...

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 ...

New

Senior Compiler Engineer - Rust GPU

Austin, TX ยท On-site

$121K - $160K/yr

We are redefining how developers write high-performance GPU software by bringing the safety, expressiveness, and modern tooling of Rust to native GPU and CUDA development. On this team, you will ...

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 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 ยท On-site

$35.50 - $39.75/hr

Description As a Silicon Prototype Engineer in Apple's GPU Design Verification and Validation organization, you'll ensure our complex GPU designs and software meet Apple's quality standards through ...

GPU Silicon Prototype Engineer

Austin, TX

$35.50 - $39.75/hr

Description As a Silicon Prototype Engineer in Apple's GPU Design Verification and Validation organization, you'll ensure our complex GPU designs and software meet Apple's quality standards through ...

GPU Silicon Prototype Engineer

Austin, TX

$35.50 - $39.75/hr

Description As a Silicon Prototype Engineer in Apple's GPU Design Verification and Validation organization, you'll ensure our complex GPU designs and software meet Apple's quality standards through ...

GPU Silicon Prototype Engineer

Austin, TX ยท On-site

$35.50 - $39.75/hr

Description As a Silicon Prototype Engineer in Apple's GPU Design Verification and Validation organization, you'll ensure our complex GPU designs and software meet Apple's quality standards through ...

Hudson River Trading (HRT) is looking for GPU Systems Engineers to help scale and evolve our exceptionally sophisticated HPC/AI research environment. Joining our Research and Development team, you ...

Engineering Group, Engineering Group > GPU ASICS Engineering General Summary: We are seeking an accomplished Principal- or Director-level technical leader to lead a high-performance RTL Design and ...

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Showing results 1-20

Gpu Engineer information

See Texas salary details

$36.3K

$94.8K

$128.1K

How much do gpu engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for gpu engineer in Texas is $94,798.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,300.00 and $108,500.00 per year, depending on experience, location, and employer.

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

To thrive as a GPU Engineer, you need strong knowledge of computer architecture, proficiency in C/C++, and experience with parallel programming models such as CUDA or OpenCL, along with a degree in computer science, electrical engineering, or a related field. Familiarity with debugging tools, driver development, performance profiling utilities, and hardware simulation platforms is typically required. Excellent problem-solving abilities, attention to detail, and effective teamwork and communication skills help distinguish top candidates. These skills ensure that GPU Engineers can develop high-performance solutions, efficiently troubleshoot hardware and software issues, and collaborate successfully in multidisciplinary environments.

What does a GPU engineer do?

A GPU Engineer designs, develops, and optimizes graphics processing units (GPUs) for applications like gaming, artificial intelligence, and high-performance computing. They work on hardware architecture, driver development, and parallel computing optimizations to maximize performance. GPU Engineers collaborate with software developers, hardware designers, and researchers to improve graphics rendering, machine learning acceleration, and computational efficiency.

What are some common challenges faced by GPU engineers, and how are they addressed?

GPU Engineers often face challenges such as optimizing code for maximum parallel efficiency, debugging complex hardware-software interactions, and keeping pace with rapidly evolving GPU architectures. Addressing these issues typically requires a combination of deep architectural understanding, use of specialized profiling and debugging tools, and ongoing collaboration with hardware, software, and QA teams. Many companies provide ongoing training and encourage knowledge sharing within engineering teams to help individuals stay current and effectively tackle new technical hurdles. Overcoming these challenges not only sharpens technical expertise but also opens doors for career growth into architect, team lead, or principal engineer roles.

What are the most commonly searched types of Gpu Engineer jobs in Texas? The most popular types of Gpu Engineer jobs in Texas are:
Infographic showing various Gpu Engineer job openings in Texas as of August 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 94% In-person, and 6% Remote job distribution, with an average salary of $94,798 per year, or $45.6 per hour.

GPU Engineer

Bot Auto

Houston, TX โ€ข On-site

Full-time

Posted 4 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.