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Cuda Programming Jobs (NOW HIRING)

Axon

Senior C++ Software Engineer

Sterling, VA

This position offers the opportunity to deepen your expertise in parallel processing and high-performance computing, with exposure to CUDA programming, positioning you at the forefront of ...

Figure

Helix AI Engineer, Perception

San Jose, CA · On-site

$200K - $400K/yr

Experience with CUDA programming and real-time systems performance optimization. * Familiarity with scalable data engineering toolchains like Spark and Ray. * Strong publication record in a relevant ...

AMD

Principal AI Performance Modeling Architect

Required : • Extensive and Senior experience optimizing large-scale ML systems and GPU architectures • Deep expertise in CUDA programming, GPU memory hierarchies, and hardware-specific ...

Axon

Senior C++ Software Engineer

Seattle, WA · On-site

This position offers the opportunity to deepen your expertise in parallel processing and high-performance computing, with exposure to CUDA programming, positioning you at the forefront of ...

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How much do cuda programming jobs pay per hour?

As of Jul 21, 2026, the average hourly pay for cuda programming in the United States is $54.36, according to ZipRecruiter salary data. Most workers in this role earn between $43.99 and $63.46 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.

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What cities are hiring for Cuda Programming jobs? Cities with the most Cuda Programming job openings:
What states have the most Cuda Programming jobs? States with the most job openings for Cuda Programming jobs include:
What job categories do people searching Cuda Programming jobs look for? The top searched job categories for Cuda Programming jobs are:
Cuda Programming jobs near you
Infographic showing various Cuda Programming job openings in the United States as of July 2026, with employment types broken down into 12% As Needed, 44% Full Time, 24% Temporary, 18% Nights, and 2% Summer. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $113,061 per year, or $54.4 per hour.
GPU Programming Expert - Fully Remote | Upto $120/hr

GPU Programming Expert - Fully Remote | Upto $120/hr

Mercor

San Francisco, CA • Remote

$120/hr

Full-time

Re-posted 16 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.