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Cuda Developer 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. The role involves collaborating with hardware architects and deep learning ...

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

Product Manager, CUDA-Q

Santa Clara, CA · On-site

The role involves driving the CUDA-Q platform, bridging classical computing and quantum processors, and requires collaboration with research and engineering teams to enhance developer experiences.

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 ...

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Cuda Developer information

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$47.4K

$110.4K

$163.8K

How much do cuda developer jobs pay per year?

As of Jun 16, 2026, the average yearly pay for cuda developer in California is $110,380.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,800.00 and $128,300.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Cuda Developers in their daily work?

Cuda Developers often encounter challenges related to optimizing code for maximum performance while managing the unique memory hierarchies and parallel processing features of GPU architectures. Debugging and profiling GPU code can be more complex than standard CPU programming, requiring specialized tools and a strong understanding of concurrency. Collaboration with data scientists, machine learning engineers, and software architects is common, as Cuda Developers often integrate their code into larger, multi-disciplinary projects. These challenges make the role both stimulating and rewarding for those who enjoy solving intricate computational problems.

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

To thrive as a Cuda Developer, you need strong proficiency in C/C++ programming, parallel computing concepts, and hands-on experience with NVIDIA CUDA architecture. Familiarity with GPU development tools such as NVIDIA Nsight, CUDA Toolkit, and relevant certifications can further validate your expertise. Problem-solving skills, attention to detail, and effective collaboration are valuable soft skills in this position. These capabilities are crucial for efficiently developing, debugging, and optimizing high-performance GPU-accelerated applications in demanding computational environments.

What is a Cuda Developer job?

A CUDA Developer is a software engineer who specializes in programming using NVIDIA's CUDA (Compute Unified Device Architecture) platform. They leverage parallel computing to optimize and accelerate applications, mainly in fields like AI, scientific computing, and high-performance computing. Their responsibilities often include writing GPU-accelerated code, optimizing performance, and working with frameworks like TensorFlow or PyTorch.

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Cuda Developer jobs near you

CUDA Kernel Optimization Specialist

Mercor

San Francisco, CA • Remote

$80 - $120/hr

Full-time

Posted 12 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.