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.
Originally posted on Himalayas