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Cuda Engineer Jobs in California (NOW HIRING)

Nvidia

Senior Software Engineer - CUDA Driver

Santa Clara, CA · On-site

$143K - $189K/yr

NVIDIA is seeking outstanding senior engineers to work on the CUDA driver, a key component of accelerated GPU computing. You will join a versatile software engineering team that delivers innovative ...

Nvidia

Senior DL Compiler Engineer -CUDA Tile

Santa Clara, CA · On-site

$143K - $189K/yr

We are hiring software engineers for the CUDA Tile team. NVIDIA GPUs are at the center of the deep learning revolution and continue to enable breakthroughs in generative AI, large language models ...

Nvidia

Senior DL Compiler Engineer -CUDA Tile

Santa Clara, CA · On-site

$143K - $189K/yr

We are hiring software engineers for the CUDA Tile team. NVIDIA GPUs are at the center of the deep learning revolution and continue to enable breakthroughs in generative AI, large language models ...

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All JobsCuda Engineer JobsCuda Engineer Jobs in California

Cuda Engineer information

See California salary details

$36K

$105.9K

$135.7K

How much do cuda engineer jobs pay per year?

As of Jul 27, 2026, the average yearly pay for cuda engineer in California is $105,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,300.00 and $134,200.00 per year, depending on experience, location, and employer.

What are CUDA Engineers?

CUDA Engineers are software developers who specialize in using NVIDIA's CUDA (Compute Unified Device Architecture) platform to write programs that run on Graphics Processing Units (GPUs). They optimize and accelerate computational tasks by parallelizing code, making use of GPUs’ capabilities for high-performance computing. CUDA Engineers often work in fields like machine learning, scientific computing, and graphics, where large amounts of data need to be processed quickly. Their expertise includes proficiency in C/C++, CUDA programming, and understanding GPU hardware and parallel computing concepts.

What is the difference between Cuda Engineer vs GPU Developer?

AspectCuda EngineerGPU Developer
Required CredentialsBachelor's or Master's in Computer Science, Engineering, or related; knowledge of CUDA, C++, parallel programmingBachelor's or Master's in Computer Science, Engineering, or related; experience with GPU programming, CUDA, OpenCL
Work EnvironmentResearch labs, tech companies, hardware firms focusing on GPU accelerationSoftware development teams, gaming, AI, scientific computing sectors
Employer & Industry UsageHardware manufacturers, AI companies, high-performance computing firmsGame development, scientific research, machine learning applications

While both roles involve GPU programming and CUDA expertise, a Cuda Engineer primarily focuses on developing and optimizing CUDA-based solutions for hardware acceleration. In contrast, a GPU Developer works on broader GPU programming tasks, including application development across various platforms. The roles often overlap but differ in scope and specific focus areas.

What are some common challenges faced by CUDA Engineers when optimizing GPU-accelerated applications?

CUDA Engineers frequently encounter challenges such as managing memory effectively between the host and the device, optimizing kernel performance, and minimizing data transfer bottlenecks. Debugging parallel code can also be complex due to race conditions and the difficulty of reproducing timing-related bugs. Collaborating closely with software developers and data scientists is essential to ensure that GPU resources are leveraged efficiently and that the application's overall performance meets project goals.

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

To thrive as a CUDA Engineer, you need a strong proficiency in C/C++ programming, parallel computing concepts, and deep knowledge of GPU architectures, often supported by a computer science or engineering degree. Experience with NVIDIA CUDA Toolkit, profiling/debugging tools, and sometimes certifications like NVIDIA DLI are highly valuable. Strong problem-solving, attention to detail, and effective communication skills help you optimize code and collaborate across teams. These skills ensure efficient development of high-performance GPU applications and successful project delivery in compute-intensive fields.
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Cuda Engineer jobs near you
Infographic showing various Cuda Engineer job openings in California as of July 2026, with employment types broken down into 3% As Needed, 63% Full Time, 4% Part Time, 18% Temporary, 2% Contract, and 10% Nights. Highlights an 90% Physical, 4% Hybrid, and 6% Remote job distribution, with an average salary of $105,877 per year, or $50.9 per hour.
CUDA Kernel Optimization Specialist

CUDA Kernel Optimization Specialist

Mercor

San Francisco, CA • Remote

$80 - $120/hr

Full-time

Posted 23 days ago

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: CUDA Engineering Expert
Type: Contract
Compensation: $80–$120/hour
Location: Remote

Role Responsibilities

  • Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
  • Use profiler metrics like L2 cache hit rate, L2 throughput, and occupancy to guide kernel improvements.
  • Review GPU kernel implementations to identify bottlenecks without needing extensive algorithmic background.
  • Write, modify, and reason about C++17, Python, and GPU programming code.
  • Apply CUDA, HIP, and shader programming expertise to improve performance outcomes.
  • Document optimization decisions clearly, noting when specific profiler metrics are useful.

Qualifications

Must-Have

  • Available to work at least 20 hrs/wk.
  • Fluent in core C++ features through C++17.
  • Working knowledge of Python and Git.
  • Fluent in at least one GPU programming model like CUDA, HIP, Slang, HLSL, or GLSL.
  • At least 1 year of professional or graduate-level research experience with GPUs.
  • Strong understanding of GPU profiler performance metrics for kernel optimization.
  • Ability to optimize GPU kernels without deep prior context on every algorithm.

Preferred

  • Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization.
  • Experience optimizing kernels for NVIDIA Blackwell hardware.
  • Familiarity with NSight Compute.
  • Prior experience with GPU hardware organizations like NVIDIA, AMD, or Qualcomm.
  • Open-source contributions related to GPU kernel optimization.

Application Process (Takes 20–30 mins to complete)

  • Submit your resume or relevant technical background to get started.
  • Qualified applicants may be asked to complete a brief technical assessment or submit additional information.

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.