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24-MAG

This opportunity is available through a leading AI-driven work platform.

CUDA & GPU Kernel Optimization Engineer

Location

United States

Posted

6 days ago

Salary

$70 - $90 / hour

Seniority

Mid Level

Job Description

CUDA & GPU Kernel Optimization Engineer

24-MAG

Role Description We are sharing a specialised part-time consulting opportunity for CUDA and GPU programming professionals experienced in kernel optimization, C++ engineering, profiler-guided performance analysis, GPU hardware utilization, and technical review. This role supports current and upcoming remote consulting opportunities focused on GPU kernel optimization, performance evaluation, CUDA/HIP review, profiler metric analysis, C++ and Python workflows, and high-quality project execution. Selected professionals will apply their GPU programming expertise to analyze kernels, identify performance bottlenecks, improve implementation quality, and document optimization decisions across modern hardware environments. Key Responsibilities - GPU Kernel Optimization - Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization. - Review kernel implementations and identify bottlenecks in memory access, occupancy, throughput, or execution patterns. - Improve performance outcomes using CUDA, HIP, shader programming, or related GPU programming models. - Optimize kernels even when limited background context is available for the underlying algorithm. - Profiler-Guided Performance Analysis - Use profiler metrics such as L2 cache hit rate, L2 throughput, occupancy, memory behavior, and related performance signals. - Evaluate when specific profiler metrics are useful, misleading, or secondary to other optimization factors. - Document optimization decisions clearly and explain tradeoffs in technical terms. - Calibrate performance judgments against structured benchmarks, hardware constraints, and project-specific criteria. - C++, Python & GPU Programming Review - Write, modify, and reason about C++17, Python, and GPU programming code. - Review code for correctness, performance impact, maintainability, and optimization potential. - Use Git-based workflows to manage technical materials and project submissions. - Apply practical GPU programming expertise across CUDA, HIP, Slang, HLSL, GLSL, or related kernel programming environments. Qualifications - Strong practical experience with GPU programming and kernel optimization. - Fluency in core C++ features through C++17. - Working knowledge of Python and Git. - Fluency in at least one GPU programming model, such as CUDA, HIP, Slang, HLSL, GLSL, or related kernel programming. - At least 1 year of professional or graduate-level research experience working with GPUs. - Strong understanding of GPU profiler performance metrics and how to use them to optimize kernels. - Ability to work independently on technical review and optimization tasks. - Availability to work at least 20 hours per week depending on project scope. Educational Background - A degree in computer science, electrical engineering, computer engineering, applied mathematics, physics, mechanical engineering, or a related technical field is helpful. - Graduate-level research, professional GPU engineering experience, or equivalent hands-on kernel optimization experience is highly relevant. - Practical experience with CUDA, HIP, GPU architecture, high-performance computing, graphics programming, or compiler-adjacent performance work may be especially valuable. Nice to Have - Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization. - Experience optimizing kernels for NVIDIA Blackwell hardware or other modern GPU architectures. - Familiarity with Nsight Compute or comparable GPU profiling tools. - Prior experience with GPU hardware organizations such as NVIDIA, AMD, Qualcomm, or similar technical environments. - Open-source contributions related to GPU kernel optimization, HPC, compiler tooling, graphics, or performance engineering. Why This Opportunity - Apply advanced GPU programming expertise to structured remote project work. - Contribute to high-quality kernel optimization, performance review, and technical evaluation workflows. - Work on flexible assignments aligned with CUDA, C++, profiler analysis, and GPU architecture strengths. - Use your ability to identify bottlenecks, improve performance, and explain optimization decisions clearly. - Remote structure with competitive hourly compensation. Contract Details - Independent contractor role. - Fully remote with flexible scheduling. - Eligible professionals may be based in approved project locations depending on project needs. - Expected commitment of at least 20 hours per week depending on project availability. - Competitive rates between $70–$90 per hour depending on expertise and project scope. - Weekly payments via Stripe or Wise. - Projects may be extended, shortened, or adjusted depending on scope and performance. - Work will not involve access to confidential or proprietary information from any employer, client, or institution. About the Platform This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams. By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy .

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