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Freelance Gpu Jobs (NOW HIRING)

This opportunity is designed for freelancers with strong C++ skills, practical GPU programming experience, and the ability to improve kernel performance using profiler-guided analysis. You'll help ...

Filmmaker / Storyteller

San Francisco, CA · On-site

$100K - $140K/yr

We currently operate the world's largest distributed GPU cluster, with over 5,000 GPUs, hundreds of ... After establishing our content foundation, recruit and manage freelancers, editors, and production ...

Freelance Gpu information

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$14

$47

$132

How much do freelance gpu jobs pay per hour?

As of Jul 3, 2026, the average hourly pay for freelance gpu in the United States is $47.71, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $61.78 per hour, depending on experience, location, and employer.

What is the difference between Freelance Gpu vs Freelance Data Scientist?

AspectFreelance GpuFreelance Data Scientist
Required CredentialsKnowledge of GPU programming, CUDA, OpenCLStatistics, programming, machine learning
Work EnvironmentProject-based, remote or on-site, tech-focusedData analysis, modeling, research
Industry UsageTech, gaming, AI, high-performance computingFinance, healthcare, marketing, tech

Freelance Gpu specialists focus on GPU programming and hardware acceleration, often working on tech and AI projects. Freelance Data Scientists analyze data to derive insights, applicable across various industries. While both roles require technical skills, their focus areas and typical projects differ significantly.

What cities are hiring for Freelance Gpu jobs? Cities with the most Freelance Gpu job openings:
What are the most commonly searched types of Gpu jobs? The most popular types of Gpu jobs are:
What states have the most Freelance Gpu jobs? States with the most job openings for Freelance Gpu jobs include:

$80 - $100/hr

Part-time

Posted 9 days ago


Job description

This role is for one of our clients
Compensation: $80-$100 per hourWe are seeking GPU kernel optimization experts to contribute to a project with a leading AI lab. This opportunity is designed for freelancers with strong C++ skills, practical GPU programming experience, and the ability to improve kernel performance using profiler-guided analysis. You'll help evaluate, optimize, and reason about GPU kernels across modern hardware environments. This is a contract-based opportunity for specialists who enjoy squeezing performance out of modern GPU architectures.
Requirements
Key Responsibilities
  • Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization
  • Use profiler metrics such as L2 cache hit rate, L2 throughput, occupancy, and related signals to guide kernel improvements
  • Review GPU kernel implementations and identify bottlenecks without requiring extensive background in the underlying algorithms
  • Write, modify, and reason about C++17, Python, and GPU programming code
  • Apply CUDA, HIP, shader programming, or related kernel programming expertise to improve performance outcomes
  • Document optimization decisions clearly, including when specific profiler metrics are or are not useful
Ideal Qualifications
  • 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, such as CUDA, HIP, Slang, HLSL, GLSL, or related kernel programming
  • At least 1 year of professional or graduate-level research experience working with GPUs
  • Strong understanding of GPU profiler performance metrics and how to use them to optimize kernels
  • Ability to optimize GPU kernels without needing deep prior context on every algorithm
  • Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization is a plus
  • Experience optimizing kernels for NVIDIA Blackwell hardware is a plus
  • Familiarity with NSight Compute is a plus
  • Prior experience with GPU hardware organizations such as NVIDIA, AMD, or Qualcomm is a plus
  • Open-source contributions related to GPU kernel optimization are a plus
4. Application Process
  • 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

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
Contract and Payment Terms
  • You will be engaged as an independent contractor.
  • This is a fully remote role that can be completed on your own schedule.
  • Projects can be extended, shortened, or concluded early depending on needs and performance.
  • Your work will not involve access to confidential or proprietary information from any employer, client, or institution.
  • Payments are weekly on Stripe or Wise based on services rendered.
  • Please note: We are unable to support H1-B or STEM OPT candidates at this time.