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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Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
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