{"schemaVersion":"jobsearcher.job.v1","id":"9e84085d609ee96b6d8ec8f8","url":"https://jobsearcher.com/jobs/9e84085d609ee96b6d8ec8f8","canonicalUrl":"https://jobsearcher.com/jobs/9e84085d609ee96b6d8ec8f8","title":"CUDA Engineer for GPU Kernel Optimization","description":"Improve the performance, efficiency, and hardware utilization of GPU kernels across modern hardware environments. This per-task contract opportunity focuses on profiler-guided optimization for specialists who enjoy extracting more performance from GPU architectures. Key Responsibilities Analyze, evaluate, and optimize GPU kernels for performance, efficiency, and hardware utilization. Use metrics such as L2 cache hit rate, L2 throughput, occupancy, and related profiler signals to guide improvements. Review kernel implementations and identify bottlenecks without requiring extensive prior knowledge of the underlying algorithms. Write, modify, and reason about C++17, Python, and GPU programming code. Apply CUDA, HIP, shader programming, or related kernel programming expertise to improve performance outcomes. Clearly document optimization decisions, including when particular profiler metrics are or are not useful. Qualifications Fluency with core C++ features through C++17. Working knowledge of Python and Git. Fluency in at least one GPU programming model, including CUDA, HIP, Slang, HLSL, GLSL, or a related kernel programming approach. At least 1 year of professional or graduate-level GPU research experience. Strong understanding of GPU profiler metrics and their use in kernel optimization. Ability to optimize GPU kernels without deep prior context for every algorithm. Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization is a plus. Experience optimizing kernels for NVIDIA Blackwell hardware is a plus. Familiarity with NSight Compute is a plus. Prior experience with GPU hardware organizations such as NVIDIA, AMD, or Qualcomm is a plus. Open-source contributions related to GPU kernel optimization are a plus. Work Terms Remote, per-task contract engagement. Availability of at least 20 hours per week is required. Compensation Compensation is 500 per task. Application Process Submit a resume or relevant technical background for consideration. Qualified applicants may be asked to complete a brief technical assessment or provide additional information.","company":"Confidential","rawCompany":"confidential","city":"New York","state":"NY","isRemote":false,"isActive":false,"createdAt":"2026-08-16T23:01:36.585Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"17-2199.00","title":"Engineers, All Other","slug":"engineers-all-other"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"CUDA Engineer for GPU Kernel Optimization","description":"Improve the performance, efficiency, and hardware utilization of GPU kernels across modern hardware environments. This per-task contract opportunity focuses on profiler-guided optimization for specialists who enjoy extracting more performance from GPU architectures. Key Responsibilities Analyze, evaluate, and optimize GPU kernels for performance, efficiency, and hardware utilization. Use metrics such as L2 cache hit rate, L2 throughput, occupancy, and related profiler signals to guide improvements. Review kernel implementations and identify bottlenecks without requiring extensive prior knowledge of the underlying algorithms. Write, modify, and reason about C++17, Python, and GPU programming code. Apply CUDA, HIP, shader programming, or related kernel programming expertise to improve performance outcomes. Clearly document optimization decisions, including when particular profiler metrics are or are not useful. Qualifications Fluency with core C++ features through C++17. Working knowledge of Python and Git. Fluency in at least one GPU programming model, including CUDA, HIP, Slang, HLSL, GLSL, or a related kernel programming approach. At least 1 year of professional or graduate-level GPU research experience. Strong understanding of GPU profiler metrics and their use in kernel optimization. Ability to optimize GPU kernels without deep prior context for every algorithm. Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization is a plus. Experience optimizing kernels for NVIDIA Blackwell hardware is a plus. Familiarity with NSight Compute is a plus. Prior experience with GPU hardware organizations such as NVIDIA, AMD, or Qualcomm is a plus. Open-source contributions related to GPU kernel optimization are a plus. Work Terms Remote, per-task contract engagement. Availability of at least 20 hours per week is required. Compensation Compensation is 500 per task. Application Process Submit a resume or relevant technical background for consideration. Qualified applicants may be asked to complete a brief technical assessment or provide additional information.","datePosted":"2026-08-16T23:01:36.585Z","dateModified":"2026-08-16T23:01:36.585Z","hiringOrganization":{"@type":"Organization","name":"Confidential","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York","addressRegion":"NY","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"9e84085d609ee96b6d8ec8f8"},"url":"https://jobsearcher.com/jobs/9e84085d609ee96b6d8ec8f8"}}