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Gpu Engineer Jobs (NOW HIRING)

Sciforium

GPU Kernel Engineer

San Francisco, CA · On-site

$190K - $250K/yr

About the role We are seeking a highly skilled GPU Kernel Engineer who is passionate about pushing the limits of performance on modern accelerators. In this role, you will design and optimize custom ...

Qualcomm

GPU Systems Driver Engineer

San Diego, CA · On-site

Engineering Group, Engineering Group > GPU ASICS Engineering General Summary: GPU System Driver Team are looking for talented software engineers to develop in-house GPU drivers to verify GPU function ...

Google

Senior Software Engineer, GPU Performance

Seattle, WA · On-site

$139K - $183K/yr

They are seeking a Senior Software Engineer to optimize GPU performance for critical products, driving innovations in AI and accelerated computing. Responsibilities : • Build optimizations for the ...

Google

Staff Software Engineer, GPU Performance

Kirkland, WA · On-site

$159K/yr

... GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques. Preferred : • Master's degree or PhD in Engineering, Computer Science, or a related technical field. • 8 ...

Google

Senior Software Engineer, GPU Performance

Kirkland, WA · On-site

$139K - $183K/yr

They are seeking a Senior Software Engineer to optimize GPU performance for critical products, driving innovations in AI and accelerated computing. Responsibilities : • Build optimizations for the ...

Anthropic

Performance Engineer, GPU

San Francisco, CA · On-site

$280K - $850K/yr

As a GPU Performance Engineer, you'll architect and implement the foundational systems that power Claude and push the frontiers of what's possible with large language models. You'll be responsible ...

Google

Software Engineer, GPU Performance

Sunnyvale, CA · On-site

$164K/yr

Experience low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques. * Experience with modern GPU architectures (NVIDIA, AMD, or other AI accelerators), memory ...

Nvidia

OR

We are now looking for a GPU Performance Engineer for Neural Reconstruction! NVIDIA is building the future of computer graphics, simulation, robotics, and embodied AI. Neural reconstruction and ...

Nvidia

We are now looking for a GPU Performance Engineer for Neural Reconstruction! NVIDIA is building the future of computer graphics, simulation, robotics, and embodied AI. Neural reconstruction and ...

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Gpu Engineer information

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$39K

$101.8K

$137.5K

How much do gpu engineer jobs pay per year?

As of Jun 29, 2026, the average yearly pay for gpu engineer in the United States is $101,752.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $116,500.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior GPU engineers, especially those with extensive experience, specialized skills in graphics architecture, and leadership roles, can earn $500,000 or more annually. High compensation often includes bonuses, stock options, and other incentives, particularly in large tech companies or specialized industries like gaming, AI, or data centers.

What engineers make $300,000 a year?

Senior GPU engineers, especially those with extensive experience, advanced skills in graphics architecture, and expertise in programming languages like C++ and CUDA, can earn $300,000 or more annually. High-level roles in large tech companies or specialized fields such as AI and machine learning often offer compensation at this level, often including bonuses and stock options.

What jobs pay $400 an hour?

High-paying roles for GPU engineers or related specialized tech positions can reach $400 an hour, typically in consulting, freelance, or contract work for companies needing advanced graphics processing or AI hardware development. Such roles often require extensive experience, advanced skills in hardware design, and a strong portfolio, with some professionals earning this rate through independent consulting or in executive technical positions.

What are the key skills and qualifications needed to thrive in the Gpu Engineer position, and why are they important?

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 and related hardware or software. They work on improving graphics performance, parallel processing, and computational efficiency, often using programming languages like C++ and tools such as CUDA or OpenCL. Their work supports applications in gaming, scientific computing, and machine learning.

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.

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Gpu Engineer jobs near you

GPU Kernel Engineer

Sciforium

San Francisco, CA • On-site

$190K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 25 days ago

Key responsibilities

  • Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas.

  • Profile and optimize end-to-end performance of ML operations, focusing on large-scale LLM training and inference.

  • Integrate low-level GPU kernels into frameworks such as PyTorch, JAX, and custom internal runtimes.

Job description

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.
About the role
We are seeking a highly skilled GPU Kernel Engineer who is passionate about pushing the limits of performance on modern accelerators. In this role, you will design and optimize custom GPU kernels that power next-generation large-scale AI systems. You will work across the hardware-software stack, from low-level kernel development to integrating optimized ops into high-level ML frameworks used for large-scale training and inference.
This role is ideal for someone who thrives at the intersection of GPU programming, systems engineering, and cutting-edge AI workloads, and who wants to make meaningful contributions to the efficiency and scalability of our ML platform.
Key Responsibilities
  • Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas.
  • Profile and optimize end-to-end performance of ML operations, with a focus on large-scale LLM training and inference.
  • Integrate low-level GPU kernels into frameworks such as PyTorch, JAX, and custom internal runtimes.
  • Develop performance models, identify bottlenecks, and deliver kernel-level improvements that significantly accelerate AI workloads.
  • Collaborate with ML researchers, distributed systems engineers, and model-serving teams to optimize compute performance across the stack.
  • Work closely with hardware vendors (NVIDIA/AMD) and stay current on the latest GPU architecture capabilities and compiler/toolchain improvements.
  • Contribute to tooling, documentation, benchmarking suites, and testing frameworks to ensure correctness and performance reproducibility.

Must-Haves
  • 5+ years of industry or research experience in GPU kernel development or high-performance computing.
  • Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related field.
  • Strong programming skills in C++, Python, and familiarity with ML frameworks.
  • Deep expertise in CUDA/ROCm, GPU memory models, and performance optimization strategies.
  • Hands-on experience with Triton and/or JAX Pallas for custom kernel development.
  • Strong understanding of PTX, GPU ASM, and low-level GPU execution.
  • Extensive experience writing and optimizing custom GPU kernels in C++ and PTX.
  • Proven ability to integrate low-level kernels into PyTorch, JAX, or similar frameworks.
  • Experience working with large-scale LLM workloads (training or inference).

Nice-to-Haves
  • Experience with AMD GPUs and ROCm optimization.
  • Familiarity with JAX FFI and custom ML operator development.
  • Experience with efficient model serving frameworks (e.g., vLLM, TensorRT).
  • Experience with TPUs, XLA, or similar accelerator programming environments.
  • Contributions to open-source ML systems, compilers, or GPU kernels.

Benefits include
  • Medical, dental, and vision insurance
  • 401k plan
  • Daily lunch, snacks, and beverages
  • Flexible time off
  • Competitive salary and equity

Equal opportunity
Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

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