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Gpu Jobs (NOW HIRING)

Performance Engineer, GPU Join to apply for the Performance Engineer, GPU role at Anthropic . About Anthropic Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We ...

GPU Kernel Developer

Redmond, UT · On-site

$45K - $121K/yr

GPU Kernel Developer City: Redmond State/Province: Washington Posting Start Date: 6/29/26 Wipro Limited (NYSE: WIT, BSE: 507685, NSE: WIPRO) is a leading technology services and consulting company ...

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 Compiler Lead

Sunnyvale, CA · On-site

$175K - $250K/yr

You'll be responsible for the GPU execution environment and transforming shader IR code into highly optimized machine code. What you'll do: * Lead the development of the LLVM-based compiler backend ...

OR · On-site

$129K - $175K/yr

NVIDIA's GPU Architecture Group is looking for architects to contribute to the design of our proprietary profiler subsystem, the apparatus embedded in every GPU that enables our profiling and ...

GPU Benchmark Analysis Architect

Cupertino, CA · On-site

$206K/yr

Description Analyze GPU workloads performance and bottlenecks various devices. Implement and/or suggest improvements to remove the identified bottlenecks. Build targeted microbenchmarks to help ...

Senior GPU Architect

Santa Clara, CA · On-site

$152K - $206K/yr

NVIDIA's GPU Architecture Group is looking for architects to contribute to the design of our proprietary profiler subsystem, the apparatus embedded in every GPU that enables our profiling and ...

GPU Software Engineer Location: San Jose, CA Duration: 6+ months contract (Long Term) Roles and Responsibilities: * As a GPU Software Engineer, you will be equipped to develop GPU IP from the early ...

$129K - $176K/yr

NVIDIA's GPU Architecture Group is looking for architects to contribute to the design of our proprietary profiler subsystem, the apparatus embedded in every GPU that enables our profiling and ...

Engineering Group, Engineering Group > GPU ASICS Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation ...

Description As a GPU performance modeling engineer, you will be responsible for developing cycle-approximate perf C/C++ models in close collaboration with architects and designers. Additionally, you ...

We are currently seeking a GPU RTL/FW Engineer for our client in the Electronics domain. We value our professionals, providing comprehensive benefits and the opportunity for growth. This is a ...

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 ...

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Gpu information

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How much do gpu jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for gpu in the United States is $54.94, according to ZipRecruiter salary data. Most workers in this role earn between $54.09 and $64.90 per hour, depending on experience, location, and employer.

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.

What is a GPU job?

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, and why are they important?

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 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 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.
More about Gpu jobs
What cities are hiring for Gpu jobs? Cities with the most Gpu job openings:
What are the most commonly searched types of Gpu jobs? The most popular types of Gpu jobs are:
What states have the most Gpu jobs? States with the most job openings for Gpu jobs include:
Infographic showing various Gpu job openings in the United States as of July 2026, with employment types broken down into 98% Full Time, 1% Part Time, and 1% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $114,281 per year, or $54.9 per hour.
Performance Engineer, GPU

Performance Engineer, GPU

Anthropic

San Francisco, CA

$315K - $560K/yr

Full-time

PTO

Posted 20 days ago


Job description

Performance Engineer, GPU

Join to apply for the Performance Engineer, GPU role at Anthropic.

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About The Role

Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. 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 for maximizing GPU utilization and performance at unprecedented scale, developing cutting‑edge optimizations that directly enable new model capabilities and dramatically improve inference efficiency.

Working at the intersection of hardware and software, you'll implement state‑of‑the‑art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack—from low‑level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.

Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world‑class researchers and engineers.

You Might Be a Good Fit If You
  • 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 interfaces to high‑level ML frameworks
  • Enjoy collaborative problem‑solving and pair programming
  • Want to work on state‑of‑the‑art language models with real‑world impact
  • Care about the societal impacts of your work
  • Thrive in ambiguous environments where you define the path forward
Strong Candidates May Also Have Experience With
  • GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization
  • ML Compilers & Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators
  • Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight
  • Distributed Systems: NCCL, NVLink, collective communication, model parallelism
  • Low‑Precision: INT8/FP8 quantization, mixed‑precision techniques
  • Production Systems: Large‑scale training infrastructure, fault tolerance, cluster orchestration
Representative Projects
  • Co‑design attention mechanisms and algorithms for next‑generation hardware architectures
  • Develop custom kernels for emerging quantization formats and mixed‑precision techniques
  • Design distributed communication strategies for multi‑node GPU clusters
  • Optimize end‑to‑end training and inference pipelines for frontier language models
  • Build performance modeling frameworks to predict and optimize GPU utilization
  • Implement kernel fusion strategies to minimize memory bandwidth bottlenecks
  • Create resilient systems for planet‑scale distributed training infrastructure
  • Profile and eliminate performance bottlenecks in production serving infrastructure
  • Partner with hardware vendors to influence future accelerator capabilities and software stacks
Deadline to apply

None. Applications will be reviewed on a rolling basis.

The Expected Salary Range For This Position Is

The expected base compensation for this position is below. Our total compensation package for full‑time employees includes equity, benefits, and may include incentive compensation.

Annual Salary

$315,000—$560,000 USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

Location‑based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How We're Different

We believe that the highest‑impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large‑scale research efforts. And we value impact — advancing our long‑term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest‑impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT‑3, Circuit‑Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Seniority Level

Mid‑Senior level

Employment Type

Full‑time

Job Function

Engineering and Information Technology

Industries

Research Services

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