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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.
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 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.
Preferred
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 .
Application Process (Takes 20–30 mins to complete)
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.
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