CUDA Kernel Optimization Specialist

๐Ÿข mercor ยท all mercor jobs
๐Ÿ“ Germany
๐Ÿ“… Posted 2026-07-05 ยท via Himalayas
๐Ÿท CUDA-Engineering,GPU-Kernel-Optimization,High-Performance-Computing,Performance-Engineering,GPU-programming,CUDA-Kernel-Engineering,CUDA-Developer,GPU-Performance-Engineer
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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: $80โ€“$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 20โ€“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.

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

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