AI Summary
Mercor is seeking a CUDA Engineering Expert to analyze and optimize GPU kernels for performance, efficiency, and hardware utilization. The ideal candidate will have experience with GPU programming models like CUDA, HIP, or Slang. The role involves working remotely and collaborating with leading AI research labs.
Key Highlights
Analyze and optimize GPU kernels for performance and efficiency
Use profiler metrics to guide kernel improvements
Write and modify C++17, Python, and GPU programming code
Key Responsibilities
Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization
Use profiler metrics like L2 cache hit rate, L2 throughput, and occupancy to guide kernel improvements
Review GPU kernel implementations to identify bottlenecks
Technical Skills Required
Benefits & Perks
Remote work
Competitive hourly compensation: $80-$120/hour
Nice to Have
Experience with CUDA C++ Core Libraries
Experience optimizing kernels for NVIDIA Blackwell hardware
Familiarity with NSight Compute
Prior experience with GPU hardware organizations like NVIDIA, AMD, or Qualcomm
Open-source contributions related to GPU kernel optimization
Job Description
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: $80–$120/hour
Location: Remote
Role Responsibilities
- Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
- Use profiler metrics like L2 cache hit rate, L2 throughput, and occupancy to guide kernel improvements.
- Review GPU kernel implementations to identify bottlenecks without needing extensive algorithmic background.
- Write, modify, and reason about C++17, Python, and GPU programming code.
- Apply CUDA, HIP, and shader programming expertise to improve performance outcomes.
- Document optimization decisions clearly, noting when specific profiler metrics are useful.
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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 at least one GPU programming model like CUDA, HIP, Slang, HLSL, or GLSL.
- At least 1 year of professional or graduate-level research experience with GPUs.
- Strong understanding of GPU profiler performance metrics for kernel optimization.
- Ability to optimize GPU kernels without deep prior context on every algorithm.
- Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization.
- Experience optimizing kernels for NVIDIA Blackwell hardware.
- Familiarity with NSight Compute.
- Prior experience with GPU hardware organizations like NVIDIA, AMD, or Qualcomm.
- Open-source contributions related to GPU kernel optimization.
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- 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.
- For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
- For any help or support, reach out to: [email protected]
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