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Gpu Programming Jobs (NOW HIRING)

Anthropic

Performance Engineer, GPU

San Francisco, CA · On-site

$280K - $850K/yr

Have deep experience with GPU programming and optimization at scale * Are impact-driven, passionate about delivering measurable performance breakthroughs * Can navigate complex systems from hardware ...

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 · On-site

Strong programming skills in Python and C++! * Hands-on experience with PyTorch or a similar tensor/autograd framework. * Experience optimizing GPU-accelerated workloads using CUDA, C++/CUDA ...

DevOpsChat

Infrastructure/GPU Engineer

Richmond, VA · On-site +1

$106K - $139K/yr

Role Summary The Infrastructure GPU Engineer position at Remotive focuses on building and supporting high-performance cloud infrastructure tailored for GPU workloads. Candidates will be responsible ...

DevOpsChat

Infrastructure/GPU Engineer

Manhattan, NY · On-site +1

$118K - $155K/yr

Role Summary The Infrastructure GPU Engineer position at Remotive focuses on building and supporting high-performance cloud infrastructure tailored for GPU workloads. Candidates will be responsible ...

ConfigUSA

CUDA Programmer

Waukesha, WI · On-site

Work with CPU-GPU parallel programming models and optimize data transfer. * Leverage NVIDIA libraries (CUDA, cuBLAS, cuDNN, NCCL as applicable). * Collaborate with system, compute, or AI/ML teams to ...

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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How much do gpu programming jobs pay per year?

As of Jun 5, 2026, the average yearly pay for gpu programming in the United States is $64,974.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $80,000.00 per year, depending on experience, location, and employer.

What is a GPU Programming job?

A GPU Programming job involves writing and optimizing code to run on Graphics Processing Units (GPUs) for parallel computing tasks. This role is commonly found in fields like machine learning, scientific computing, gaming, and data analytics. GPU programmers use languages such as CUDA, OpenCL, or Vulkan to accelerate computations and improve performance. They work closely with software engineers and data scientists to optimize algorithms for high-performance applications.

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

To excel in GPU Programming, you need a strong background in parallel computing concepts, mathematics, and proficiency in languages such as CUDA, OpenCL, or DirectX/OpenGL, often supported by a degree in computer science, engineering, or a related field. Familiarity with NVIDIA and AMD GPU development tools, performance profilers, and possibly certifications like NVIDIA's Deep Learning Institute courses are valuable. Teamwork, effective communication, and strong problem-solving abilities are essential soft skills in this field. These competencies enable efficient development, optimization, and integration of high-performance GPU code in real-world applications.

What types of projects or applications do GPU Programmers commonly work on?

GPU Programmers are often involved in developing or optimizing software for high-performance applications such as machine learning, scientific simulations, real-time rendering in gaming and visualization, and video/image processing tools. Their daily work may include collaborating with software engineers, data scientists, and hardware teams to create efficient, scalable parallel algorithms that leverage GPU capabilities. The role frequently requires problem-solving to maximize computational efficiency and troubleshooting complex performance bottlenecks. By working across multidisciplinary teams, GPU Programmers help deliver robust solutions for data-intensive problems in areas like healthcare, finance, automotive technology, and entertainment.

More about Gpu Programming jobs
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Gpu Programming jobs near you
Infographic showing various Gpu Programming job openings in the United States as of May 2026, with employment types broken down into 83% Full Time, 13% Part Time, 3% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $64,974 per year, or $31.2 per hour.
GPU Software Architecture Engineer, Graphics, Games, & ML

GPU Software Architecture Engineer, Graphics, Games, & ML

Apple

Cupertino, CA • On-site

$172K - $213K/yr

Full-time

Posted 11 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Apple Silicon GPU SW architecture team within the Media, Graphics & Compute Technologies group is seeking a senior/principal engineer to lead server-side ML acceleration and multi-node distribution initiatives. You will help define and shape our future GPU compute infrastructure on Private Cloud Compute that enables Apple Intelligence.
In this role, you'll be at the forefront of architecting and building our next-generation distributed ML infrastructure, where you'll tackle the complex challenge of orchestrating massive network models across server clusters to power Apple Intelligence at unprecedented scale. It will involve designing sophisticated parallelization strategies that split models across many GPUs, optimizing every layer of the stack-from low-level memory access patterns to high-level distributed algorithms-to achieve maximum hardware utilization while minimizing latency for real-time user experiences. You'll work at the intersection of cutting-edge ML systems and hardware acceleration, collaborating directly with silicon architects to influence future GPU designs based on your deep understanding of inference workload characteristics, while simultaneously building the production systems that will serve billions of requests daily.This is a hands-on technical leadership position where you'll not only architect these systems but also dive deep into performance profiling, implement novel optimization techniques, and solve unprecedented scaling challenges as you help define the future of AI experiences delivered through Apple's secure cloud infrastructure.
10+ years of experience in GPU programming (CUDA, ROCm) and high-performance computing, successfully optimizing large-scale parallel workloads.Strong experience with inter-node communication technologies (InfiniBand, RDMA, NCCL) in the context of ML training/inferenceMust have excellent system programming skills in C/C++Deep understanding of distributed systems and parallel computing architecturesUnderstand how tensor frameworks (PyTorch, JAX, TensorFlow) are used in distributed training/inferenceBachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field
Familiar with model development lifecycle from trained model to large scale production inference deploymentProven track record in ML infrastructure at scalePython is a plusPhD in Computer Science, Engineering, Mathematics, or a related technical field

What Apple employees say

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Benefits

Hours and flexibility

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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