GPU Programming Expert - Fully Remote | Upto $500/task Task based
关于工作的职位，默科尔将精英创意和技术人才与领先的人工智能研究实验室联系起来。总部位于旧金山，我们的投资者包括 Benchmark,General Catalyst,Peter Thiel,Adam D'Angelo,Larry Summers 和 Jack Dorsey.职位：CUDA 工程专家类型：合同补偿：500美元/小时位置：远程角色责任 ·分析和优化 GPU 内核以实现性能，效率和硬件利用。·使用 L2缓存击率和占用量等配置文件指标来引导内核改进。·审查 GPU 内实施并识别没有深入算法背景的瓶。·写和修改 C++17,Python 和 GPU 编程代码。· 应用 CUDA,HIP 和影子编程专业知识以提高性能。· 文件优化决策明确，重点放在配置文件的指标的实用性上。必须有资格。·可至少20小时/周工作。· 流利使用 C++核心功能通过 C++17. · 熟悉 Python 和 Git. · 一个 GPU 编程模型如 CUDA 或 HIP. · 1年以上与 GPU 的专业或研究经验。· 强大的了解 GPU 表现指标。首选；• 与 CUDA,Hip 和 CUDA C++核心图书馆的经验。· 经验与 NVIDIA,AMD 或高通。· 与 GPU 内核优化相关的开源贡献。申请过程 （需要20~30分钟才能完成）·提交简历或相关技术背景。·合格的申请人可以完成简短的技术评估或提交额外信息。资源和支持 ·有关面试进程和平台信息的详细信息请查看：·任何帮助或支持，联系：PS:我们的团队每天审查应用程序。请完成你的 AI 面试和申请步骤，以便考虑此机会。最初发布在喜马拉雅山。
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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:$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