ZipRecruiter

Log In

1

Cuda Programming Jobs in California (NOW HIRING)

MatchPoint Solutions

Embedded AI Engineer

Sunnyvale, CA · On-site

$156K - $206K/yr

... with CUDA programming and PyTorch framework • In-depth knowledge of deep learning models, particularly Large Language Models (LLMs) • Proficiency in C++ and Python programming languages • ...

Genmo

GPU Performance Engineer

San Francisco, CA · On-site

Strong CUDA programming skills with production kernel development * Deep understanding of GPU architecture (memory hierarchy, SMs, warps) * Track record of achieving significant performance ...

Genmo

GPU Performance Engineer

San Francisco, CA · On-site

Strong CUDA programming skills with production kernel development * Deep understanding of GPU architecture (memory hierarchy, SMs, warps) * Track record of achieving significant performance ...

PRAGMATIKE

CUDA Kernel Engineer

San Francisco, CA · Remote

We are searching for a CUDA Kernel Engineer who has hands-on experience developing and optimizing NVIDIA CUDA kernels from scratch . You will work on the GPU performance layer powering large-scale ...

Nvidia

Senior Software Engineer - CUDA Driver

Santa Clara, CA · On-site

$143K - $189K/yr

Define forward-looking improvements to the CUDA APIs and programming model * Build and maintain performance and precision modeling * Write effective, maintainable, and well-tested code * Develop code ...

Nvidia

System Software Engineer - CUDA Chips

Santa Clara, CA · On-site

$203K - $240K/yr

CUDA helps define a unified programming model across a range of system configurations and hardware capabilities accomplished through CUDA driver interaction with GPU hardware, kernel mode drivers ...

next page

Showing results 1-20

All JobsCuda Programming JobsCuda Programming Jobs in California

Cuda Programming information

See California salary details

$27

$53

$80

How much do cuda programming jobs pay per hour?

As of Jul 5, 2026, the average hourly pay for cuda programming in California is $53.64, according to ZipRecruiter salary data. Most workers in this role earn between $43.41 and $62.64 per hour, depending on experience, location, and employer.

What is the salary of NVIDIA CUDA developer?

The salary of an NVIDIA CUDA developer typically ranges from $80,000 to $130,000 annually, depending on experience, location, and industry. Skilled CUDA programmers with advanced knowledge of parallel computing and GPU architecture tend to earn higher salaries.

What jobs use CUDA?

Jobs that use CUDA include roles such as GPU programmer, software developer, data scientist, and machine learning engineer, especially in fields like high-performance computing, artificial intelligence, and scientific research. These roles often require knowledge of parallel programming, C++, and GPU architecture, and involve developing or optimizing software to run efficiently on NVIDIA GPUs.

Are CUDA programmers in demand?

CUDA programmers are in high demand due to the growing use of GPU computing in fields like artificial intelligence, scientific research, and data processing. Skills in parallel programming, GPU architecture, and CUDA toolkit are highly valued, and job opportunities are expected to increase as these technologies expand across industries.

How much do CUDA engineers make?

CUDA engineers typically earn between $80,000 and $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in parallel programming and GPU optimization can command higher salaries, especially in tech hubs or companies with advanced AI and high-performance computing needs.

What is the difference between Cuda Programming vs GPU Developer?

AspectCuda ProgrammingGPU Developer
Required CredentialsKnowledge of CUDA, C/C++, parallel computingKnowledge of GPU architecture, CUDA, OpenCL, C/C++
Work EnvironmentHigh-performance computing, scientific research, AIGraphics, gaming, scientific visualization, AI
Industry UsageTech companies, research labs, AI firmsGaming, entertainment, tech, research

While Cuda Programming focuses specifically on writing code using NVIDIA's CUDA platform for parallel processing, GPU Developers have a broader role that includes designing, optimizing, and implementing GPU-based solutions across various platforms and technologies. Both roles require knowledge of GPU architecture and programming languages like C/C++, but GPU Developers often work on a wider range of applications beyond CUDA-specific projects.

What are popular job titles related to Cuda Programming jobs in California? For Cuda Programming jobs in California, the most frequently searched job titles are:
What job categories do people searching Cuda Programming jobs in California look for? The top searched job categories for Cuda Programming jobs in California are:
What cities in California are hiring for Cuda Programming jobs? Cities in California with the most Cuda Programming job openings:
Cuda Programming jobs near you
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 20 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.
Nvidia logo

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

View All Nvidia Jobs

Apply