CUDA Engineer - Kernel Optimization
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:$500/hour Location:Remote
Role Responsibilities
- Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
- Use profiler metrics like L2 cache hit rate and occupancy to guide kernel improvements.
- Review GPU kernel implementations and identify bottlenecks without deep algorithmic background.
- Write and modify C++17, Python, and GPU programming code.
- Apply expertise in CUDA, HIP, and shader programming to improve performance.
- Document optimization decisions clearly, focusing on profiler metrics' utility.
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 one GPU programming model like CUDA or HIP.
- 1+ year of professional or research experience with GPUs.
- Strong understanding of GPU profiler performance metrics.
Preferred
- Experience with CUDA, HIP, and CUDA C++ Core Libraries.
- Experience optimizing kernels for NVIDIA Blackwell hardware.
- Familiarity with NSight Compute.
- Prior experience with 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.
- Qualified applicants may complete a brief technical assessment or submit additional information.
Resources & Support
- For details about the interview process and platform information, please check:
- For any help or support, reach out to:
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
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