Contract GPU kernel optimization role: analyze profiler metrics, identify bottlenecks, and improve kernel performance across modern GPU hardware. Implement and modify C++17, Python, and GPU code using CUDA/HIP/shaders, document optimization decisions, and collaborate as a freelance specialist (20+ hrs/week preferred).
This role is for one of our clients
Compensation: $80-$100 per hour
We 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.
RequirementsKey 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
- 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
- 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.
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