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Cuda Jobs (NOW HIRING)

Nvidia

Senior DL Compiler Engineer- CUDA Tile

Redmond, WA

$137K - $180K/yr

CUDA Tile shipped with CUDA 13.1 and is a major addition to CUDA ( * You will design and implement compiler transformations, develop MLIR-based dialects and lowering passes, and optimize the ...

Nvidia

OR

$122K - $161K/yr

Our team is dedicated to exploring and prototyping next-generation ideas that bridge the gap between deep learning algorithms and CUDA, pushing the boundaries of what is possible on modern ...

Nvidia

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NVIDIA's core CUDA math libraries are the foundation of NVIDIA CUDA-X libraries. This position will require leading at the intersection of Engineering, Product, and our customers to advocate ...

Nvidia

OR

$122K - $161K/yr

NVIDIA is seeking a Senior Software Engineer, NCCL and CUDA specialization to join our Cloud Service Provider (CSP)Engagements team, focusing on ML software stack functionality and performance for ...

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How much do cuda jobs pay per year?

As of Jun 7, 2026, the average yearly pay for cuda in the United States is $200,510.00, according to ZipRecruiter salary data. Most workers in this role earn between $205,000.00 and $205,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced when working as a CUDA Developer, and how can they be addressed?

CUDA Developers often encounter challenges such as debugging complex parallel code, optimizing memory usage, and ensuring compatibility across different GPU architectures. To address these, it's important to leverage profiling tools like NVIDIA Nsight to identify bottlenecks and inefficiencies. Collaborating closely with team members, such as data scientists and software engineers, can also help in resolving integration issues and achieving better performance. Staying updated with the latest CUDA Toolkit releases and best practices is key to overcoming these challenges and delivering robust GPU-accelerated applications.

What are the key skills and qualifications needed to thrive as a CUDA Developer, and why are they important?

To thrive as a CUDA Developer, you need strong programming skills in C/C++, a solid understanding of parallel computing concepts, and experience with GPU architectures. Familiarity with the CUDA toolkit, NVIDIA GPUs, and related profiling/debugging tools is typically required, and certifications in GPU programming can be advantageous. Analytical thinking, problem-solving, and effective communication are essential soft skills for optimizing code and collaborating with cross-functional teams. These skills are crucial for developing high-performance applications that leverage GPU acceleration, ensuring efficiency and innovation in compute-intensive fields.

What is the difference between Cuda vs GPU Developer?

AspectCudaGPU Developer
Required CredentialsKnowledge of CUDA programming, often with a background in computer science or engineeringExperience with GPU programming, CUDA, OpenCL, or similar; often requires a degree in computer science or related fields
Work EnvironmentPrimarily focused on developing and optimizing CUDA-based applications for NVIDIA GPUsDesigning, developing, and maintaining GPU-accelerated applications across various platforms and hardware
Industry UsageUsed mainly in high-performance computing, AI, and scientific research involving NVIDIA GPUsApplied across gaming, scientific computing, AI, and multimedia industries

In summary, CUDA is a specialized skill set focused on programming NVIDIA GPUs using CUDA, while a GPU Developer has a broader role that may include using various GPU programming tools and working across multiple platforms. CUDA is a subset of the skills a GPU Developer might possess, making them closely related but distinct roles.

What is a Cuda job?

A CUDA job typically involves developing, optimizing, and implementing parallel computing applications using NVIDIA's CUDA platform. CUDA (Compute Unified Device Architecture) enables developers to leverage the power of GPUs for high-performance computing tasks such as deep learning, simulations, and scientific computing. Professionals in this role often work with C, C++, or Python, using CUDA libraries and frameworks to accelerate processing. Strong knowledge of parallel programming, memory management, and GPU architecture is essential for success in this field.

What are CUDA developers?

CUDA developers are software engineers who specialize in using NVIDIA's CUDA (Compute Unified Device Architecture) platform to write programs that run on Graphics Processing Units (GPUs). Their primary focus is on parallel computing, optimizing algorithms to leverage GPU acceleration for tasks such as scientific computing, machine learning, and data processing. These professionals typically have strong skills in C, C++, and Python, and a solid understanding of GPU hardware. CUDA developers are in demand in industries that require high-performance computing solutions.

How much do CUDA programmers make?

CUDA programmers, who develop software using NVIDIA's parallel computing platform, typically earn between $80,000 and $130,000 annually depending on experience, location, and industry. Senior roles or those with specialized skills in GPU optimization and machine learning can earn higher salaries, especially in tech hubs or large companies.
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Cuda jobs near you
Infographic showing various Cuda job openings in the United States as of May 2026, with employment types broken down into 98% Full Time, 1% Part Time, and 1% Contract. Highlights an 87% Physical, 6% Hybrid, and 7% Remote job distribution, with an average salary of $200,510 per year, or $96.4 per hour.
Senior Software Engineer, CUDA Deep Learning Systems

Senior Software Engineer, CUDA Deep Learning Systems

NVIDIA

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Posted 22 days ago

Job description

Job Summary:
NVIDIA is a leading technology company focused on pioneering initiatives in artificial intelligence and deep learning systems. They are seeking a Senior Software Engineer to work on optimizing CUDA and Deep Learning Systems, exploring novel systems optimizations, and collaborating with AI researchers to enhance hardware performance for AI workloads.
Responsibilities:
• Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping.
• Architect and optimize distributed computing systems that scale seamlessly from a single node to massive, cluster-scale supercomputing environments.
• Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads.
• Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines.
• Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization, memory bandwidth, cross-node network communication efficiency and programmability.
• Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning.
• Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases, upstream framework integrations, internal tools, or closed-source commercial products.
Qualifications:
Required:
• BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
• 8+ years of relevant industry experience or equivalent academic experience after degree achievement.
• Strong proficiency in C++ and Python programming.
• Solid background in the fundamentals of Deep Learning with a focus on transformers.
• Strong understanding of distributed computing principles, multi-node scaling, and the unique performance challenges of cluster-scale execution.
• Proven experience in systems programming, computer architecture, and low-level systems performance optimization.
• Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming and kernel optimization.
• A strong analytical approach with experience using profiling tools to deeply understand software performance on hardware.
• Experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models.
• Background in deep learning compilers, both graph-level and codegen (e.g., Triton, XLA, torch compile)
Preferred:
• Deep expertise in the performance internals and execution graphs of major deep learning autograd, training and inference frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron, MaxText, etc.).
• Hands-on experience with CUDA, communication libraries (e.g., NCCL, MPI, UCX) and distributed machine learning techniques (e.g., pipeline parallelism, tensor parallelism).
• Knowledge of numerical methods, low-precision arithmetic (e.g., NVFP4, MXFP4, FP8, INT8), and their implications on deep learning model accuracy and performance.
• Familiarity with systems requirements for Reinforcement Learning (RL) or highly parallel simulation environments and/or research background in machine learning systems or adjacent fields.
• Experience with machine learning, especially agentic systems, applied to systems problems.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.
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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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